Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ₁ Regularization.

Science.gov (United States)

Hernandez-Suarez, Aldo; Sanchez-Perez, Gabriel; Toscano-Medina, Karina; Martinez-Hernandez, Victor; Perez-Meana, Hector; Olivares-Mercado, Jesus; Sanchez, Victor

2018-04-29

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

Science.gov (United States)

Sanchez-Perez, Gabriel; Toscano-Medina, Karina; Martinez-Hernandez, Victor; Olivares-Mercado, Jesus; Sanchez, Victor

2018-01-01

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization. PMID:29710833

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

Directory of Open Access Journals (Sweden)

Aldo Hernandez-Suarez

2018-04-01

Full Text Available In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

A pattern-matched Twitter analysis of US cancer-patient sentiments.

Science.gov (United States)

Crannell, W Christian; Clark, Eric; Jones, Chris; James, Ted A; Moore, Jesse

2016-12-01

Twitter has been recognized as an important source of organic sentiment and opinion. This study aimed to (1) characterize the content of tweets authored by the United States cancer patients; and (2) use patient tweets to compute the average happiness of cancer patients for each cancer diagnosis. A large sample of English tweets from March 2014 through December 2014 was obtained from Twitter. Using regular expression software pattern matching, the tweets were filtered by cancer diagnosis. For each cancer-specific tweetset, individual patients were extracted, and the content of the tweet was categorized. The patients' Twitter identification numbers were used to gather all tweets for each patient, and happiness values for patient tweets were calculated using a quantitative hedonometric analysis. The most frequently tweeted cancers were breast (n = 15,421, 11% of total cancer tweets), lung (n = 2928, 2.0%), prostate (n = 1036, 0.7%), and colorectal (n = 773, 0.5%). Patient tweets pertained to the treatment course (n = 73, 26%), diagnosis (n = 65, 23%), and then surgery and/or biopsy (n = 42, 15%). Computed happiness values for each cancer diagnosis revealed higher average happiness values for thyroid (h_avg = 6.1625), breast (h_avg = 6.1485), and lymphoma (h_avg = 6.0977) cancers and lower average happiness values for pancreatic (h_avg = 5.8766), lung (h_avg = 5.8733), and kidney (h_avg = 5.8464) cancers. The study confirms that patients are expressing themselves openly on social media about their illness and that unique cancer diagnoses are correlated with varying degrees of happiness. Twitter can be employed as a tool to identify patient needs and as a means to gauge the cancer patient experience. Copyright © 2016 Elsevier Inc. All rights reserved.

Contempt, like any other social affect, can be an emotion as well as a sentiment.

Science.gov (United States)

Giner-Sorolla, Roger; Fischer, Agneta H

2017-01-01

Gervais & Fessler assert that contempt is (a) not an emotion (or an attitude) but (b) a sentiment. Here, we challenge the validity and empirical basis of these two assertions, arguing that contempt, like many other emotions, can be both an emotion and a sentiment.

Contempt, like any other social affect, can be an emotion as well as a sentiment

NARCIS (Netherlands)

Giner-Sorolla, R.; Fischer, A.H.

2017-01-01

Gervais and Fessler assert that contempt is (a) not an emotion (or an attitude), but (b) a sentiment. Here, we challenge the validity and empirical basis of these two assertions, arguing that contempt, as many other emotions, can be both an emotion and sentiment.

Microblog sentiment analysis using social and topic context.

Science.gov (United States)

Zou, Xiaomei; Yang, Jing; Zhang, Jianpei

2018-01-01

Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.

Using Tweets for Assigning Sentiments to Regions

NARCIS (Netherlands)

Tjong Kim Sang, Erik

2014-01-01

We derive a sentiment lexicon for Dutch tweets and apply the lexicon for classifying Dutch tweets as positive, negative or neutral. The classifier enables us to test what regions in the Netherlands and Flanders express more positive sentiment on Twitter than others. The results reveal sentiment

The (Un)Predictability of Emotional Hashtags in Twitter

NARCIS (Netherlands)

Kunneman, F.A.; Liebrecht, C.C.; Bosch, A.P.J. van den

2014-01-01

Hashtags in Twitter posts may carry different semantic payloads. Their dual form (word and label) may serve to categorize the tweet, but may also add content to the message, or strengthen it. Some hashtags are related to emotions. In a study on emotional hashtags in Dutch Twitter posts we employ

On the deep structure of social affect: Attitudes, emotions, sentiments, and the case of "contempt".

Science.gov (United States)

Gervais, Matthew M; Fessler, Daniel M T

2017-01-01

Contempt is typically studied as a uniquely human moral emotion. However, this approach has yielded inconclusive results. We argue this is because the folk affect concept "contempt" has been inaccurately mapped onto basic affect systems. "Contempt" has features that are inconsistent with a basic emotion, especially its protracted duration and frequently cold phenomenology. Yet other features are inconsistent with a basic attitude. Nonetheless, the features of "contempt" functionally cohere. To account for this, we revive and reconfigure the sentiment construct using the notion of evolved functional specialization. We develop the Attitude-Scenario-Emotion (ASE) model of sentiments, in which enduring attitudes represent others' social-relational value and moderate discrete emotions across scenarios. Sentiments are functional networks of attitudes and emotions. Distinct sentiments, including love, respect, like, hate, and fear, track distinct relational affordances, and each is emotionally pluripotent, thereby serving both bookkeeping and commitment functions within relationships. The sentiment contempt is an absence of respect; from cues to others' low efficacy, it represents them as worthless and small, muting compassion, guilt, and shame and potentiating anger, disgust, and mirth. This sentiment is ancient yet implicated in the ratcheting evolution of human ultrasocialty. The manifolds of the contempt network, differentially engaged across individuals and populations, explain the features of "contempt," its translatability, and its variable experience as "hot" or "cold," occurrent or enduring, and anger-like or disgust-like. This rapprochement between psychological anthropology and evolutionary psychology contributes both methodological and empirical insights, with broad implications for understanding the functional and cultural organization of social affect.

Sexed Sentiments: Interdisciplinary Perspectives on Gender and Emotion

NARCIS (Netherlands)

Ruberg, W. G.; Steenbergh, K.

2011-01-01

Sexed Sentiments provides a gender perspective on the recent turn to affect in criticism. It presents new work by scholars from different disciplines working on gender and emotion, a field par excellence where an interdisciplinary focus is fruitful. This collection presents essays from disciplines

Content Analysis of Tobacco-related Twitter Posts

Science.gov (United States)

Myslín, Mark; Zhu, Shu-Hong; Conway, Michael

2013-01-01

Objective We present results of a content analysis of tobacco-related Twitter posts (tweets), focusing on tweets referencing e-cigarettes and hookah. Introduction Vast amounts of free, real-time, localizable Twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particularly in emerging areas of public health concern where reliable statistical evidence is not readily accessible. Existing applications include tracking public informedness during disease outbreaks [1]. Twitter-based surveillance is particularly suited to new challenges in tobacco control. Hookah and e-cigarettes have surged in popularity, yet regulation and public information remain sparse, despite controversial health effects [2,3]. Ubiquitous online marketing of these products and their popularity among new and younger users make Twitter a key resource for tobacco surveillance. Methods We collected 7,300 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012, using ten general keywords such as cig* and hookah. Each tweet was manually classified using a tri-axial scheme, capturing genre (firsthand experience, joke, news, …), theme (underage usage, health, social image, …), and sentiment (positive, negative, neutral). Machine-learning classifiers were trained to detect tobacco-related vs. irrelevant tweets as well as each of the above categories, using Naïve Bayes, k-Nearest Neighbors, and Support Vector Machine algorithms. Finally, phi correlation coefficients were computed between each of the categories to discover emergent patterns. Results The most prevalent genre of tweets was personal experience, followed by categories such as opinion, marketing, and news. The most common themes were hookah, cessation, and social image, and sentiment toward tobacco was more positive (26%) than negative (20%). The most highly correlated categories were social image�

Maintaining Sentiment Polarity in Translation of User-Generated Content

Directory of Open Access Journals (Sweden)

Lohar Pintu

2017-06-01

Full Text Available The advent of social media has shaken the very foundations of how we share information, with Twitter, Facebook, and Linkedin among many well-known social networking platforms that facilitate information generation and distribution. However, the maximum 140-character restriction in Twitter encourages users to (sometimes deliberately write somewhat informally in most cases. As a result, machine translation (MT of user-generated content (UGC becomes much more difficult for such noisy texts. In addition to translation quality being affected, this phenomenon may also negatively impact sentiment preservation in the translation process. That is, a sentence with positive sentiment in the source language may be translated into a sentence with negative or neutral sentiment in the target language. In this paper, we analyse both sentiment preservation and MT quality per se in the context of UGC, focusing especially on whether sentiment classification helps improve sentiment preservation in MT of UGC. We build four different experimental setups for tweet translation (i using a single MT model trained on the whole Twitter parallel corpus, (ii using multiple MT models based on sentiment classification, (iii using MT models including additional out-of-domain data, and (iv adding MT models based on the phrase-table fill-up method to accompany the sentiment translation models with an aim of improving MT quality and at the same time maintaining sentiment polarity preservation. Our empirical evaluation shows that despite a slight deterioration in MT quality, our system significantly outperforms the Baseline MT system (without using sentiment classification in terms of sentiment preservation. We also demonstrate that using an MT engine that conveys a sentiment different from that of the UGC can even worsen both the translation quality and sentiment preservation.

Social media sentiment and consumer confidence

OpenAIRE

Daas, Piet J.H.; Puts, Marco J.H.

2014-01-01

Changes in the sentiment of Dutch public social media messages were compared with changes in monthly consumer confidence over a period of three-and-a-half years, revealing that both were highly correlated (up to r = 0.9) and that both series cointegrated. This phenomenon is predominantly affected by changes in the sentiment of all Dutch public Facebook messages. The inclusion of various selections of public Twitter messages improved this association and the response to changes in sentiment. G...

Twitter and traumatic brain injury: A content and sentiment analysis of tweets pertaining to sport-related brain injury.

Science.gov (United States)

Workewych, Adriana M; Ciuffetelli Muzzi, Madeline; Jing, Rowan; Zhang, Stanley; Topolovec-Vranic, Jane; Cusimano, Michael D

2017-01-01

Sport-related traumatic brain injuries are a significant public health burden, with hundreds of thousands sustained annually in North America. While sports offer numerous physical and social health benefits, traumatic brain injuries such as concussion can seriously impact a player's life, athletic career, and sport enjoyment. The culture in many sports encourages winning at all costs, placing athletes at risk for traumatic brain injuries. As social media has become a central part of everyday life, the content of users' messages often reflects the prevailing culture related to a particular event or health issue. We hypothesized that Twitter data might be useful for understanding public perceptions and misperceptions of sport-related traumatic brain injuries. We performed a content and sentiment analysis of 7483 Twitter ® tweets related to traumatic brain injuries in sports collected during June and July 2013. We identified five major themes. Users tweeted about personal traumatic brain injuries experiences, reported traumatic brain injuries in professional athletes, shared research about sport-related concussions, and discussed policy and safety in injury prevention, such as helmet use. We identified mixed perceptions of and sentiment toward traumatic brain injuries in sports: both an understanding that brain injuries are serious and disregard for activities that might reduce the public burden of traumatic brain injuries were prevalent in our Twitter analysis. While the scientific and medical community considers a concussion a form of traumatic brain injuries, our study demonstrates a misunderstanding of this fact among the public. In our current digital age, social media can provide useful insight into the culture around a health issue, facilitating implementation of prevention and treatment strategies.

Methods for Coding Tobacco-Related Twitter Data: A Systematic Review.

Science.gov (United States)

Lienemann, Brianna A; Unger, Jennifer B; Cruz, Tess Boley; Chu, Kar-Hai

2017-03-31

As Twitter has grown in popularity to 313 million monthly active users, researchers have increasingly been using it as a data source for tobacco-related research. The objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies. Data sources included PsycINFO, Web of Science, PubMed, ABI/INFORM, Communication Source, and Tobacco Regulatory Science. Searches were limited to peer-reviewed journals and conference proceedings in English from January 2006 to July 2016. The initial search identified 274 articles using a Twitter keyword and a tobacco keyword. One coder reviewed all abstracts and identified 27 articles that met the following inclusion criteria: (1) original research, (2) focused on tobacco or a tobacco product, (3) analyzed Twitter data, and (4) coded Twitter data categorically. One coder extracted data collection and coding methods. E-cigarettes were the most common type of Twitter data analyzed, followed by specific tobacco campaigns. The most prevalent data sources were Gnip and Twitter's Streaming application programming interface (API). The primary methods of coding were hand-coding and machine learning. The studies predominantly coded for relevance, sentiment, theme, user or account, and location of user. Standards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter's databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations. ©Brianna A Lienemann, Jennifer B Unger, Tess Boley Cruz, Kar-Hai Chu. Originally published in the

Online Influence and Sentiment of Fitness Tweets: Analysis of Two Million Fitness Tweets.

Science.gov (United States)

Vickey, Theodore; Breslin, John G

2017-10-31

by a ratio of 4 to 1. The results of this research suggest that the users of mobile fitness apps who share their workouts via Twitter have a lower Klout Score than the general Twitter user and that users who chose to share additional insights into their workouts are more positive in sentiment than negative. We present a novel perspective into the physical activity messaging from within mobile fitness apps that are then shared over Twitter. By moving beyond the numbers and evaluating both the Twitter user and the emotions tied to physical activity, future research could analyze additional relationships between the user's online influence, the enjoyment of the physical activity, and with additional analysis a long-term retention strategy for the use of a fitness app. ©Theodore Vickey, John G. Breslin. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 31.10.2017.

Assessing mobile health applications with twitter analytics.

Science.gov (United States)

Pai, Rajesh R; Alathur, Sreejith

2018-05-01

Advancement in the field of information technology and rise in the use of Internet has changed the lives of people by enabling various services online. In recent times, healthcare sector which faces its service delivery challenges started promoting and using mobile health applications with the intention of cutting down the cost making it accessible and affordable to the people. The objective of the study is to perform sentiment analysis using the Twitter data which measures the perception and use of various mobile health applications among the citizens. The methodology followed in this research is qualitative with the data extracted from a social networking site "Twitter" through a tool RStudio. This tool with the help of Twitter Application Programming Interface requested one thousand tweets each for four different phrases of mobile health applications (apps) such as "fitness app", "diabetes app", "meditation app", and "cancer app". Depending on the tweets, sentiment analysis was carried out, and its polarity and emotions were measured. Except for cancer app there exists a positive polarity towards the fitness, diabetes, and meditation apps among the users. Following a system thinking approach for our results, this paper also explains the causal relationships between the accessibility and acceptability of mobile health applications which helps the healthcare facility and the application developers in understanding and analyzing the dynamics involved the adopting a new system or modifying an existing one. Copyright © 2018 Elsevier B.V. All rights reserved.

A new ANEW: Evaluation of a word list for sentiment analysis in microblogs

DEFF Research Database (Denmark)

Nielsen, Finn Årup

2011-01-01

Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, — a “sentiment lexicon” or “affective word...... lists”. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison...... with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength....

Differential Emotions and the Stock Market - The Case of Company-Specific Trading

DEFF Research Database (Denmark)

Risius, Marten; Akolk, Fabian; Beck, Roman

2015-01-01

in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness......, satisfaction, fear, anger, depression, and contempt). To investigate the connection between the differential emotions and stock movements we analyze approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months...... emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength is associated with a significant decrease in company-specific stock prices....

Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns

OpenAIRE

Mahmud, Jalal; Gao, Huiji

2014-01-01

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers sentiment towards the campaign topic and their rate of retweeting of messages generated by the campaign. Our analysis with followers of multiple social-media campaigns found statistical significant correlations between such sentiment ...

Two kinds of respect for two kinds of contempt: Why contempt can be both a sentiment and an emotion.

Science.gov (United States)

Cova, Florian; Deonna, Julien; Sander, David; Teroni, Fabrice

2017-01-01

Gervais & Fessler argue that because contempt is a sentiment, it cannot be an emotion. However, like many affective labels, it could be that "contempt" refers both to a sentiment and to a distinct emotion. This possibility is made salient by the fact that contempt can be defined by contrast with respect, but that there are different kinds of respect.

Sentiment analysis and the impact of employee satisfaction on firm earnings

NARCIS (Netherlands)

A.J. Moniz (Andy); F.M.G. de Jong (Franciska)

2014-01-01

textabstractPrior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders - the media and consumers. In this study we examine the sentiment of a potentially overlooked

Climate Change, Disaster and Sentiment Analysis over Social Media Mining

Science.gov (United States)

Lee, J.; McCusker, J. P.; McGuinness, D. L.

2012-12-01

Accelerated climate change causes disasters and disrupts people living all over the globe. Disruptive climate events are often reflected in expressed sentiments of the people affected. Monitoring changes in these sentiments during and after disasters can reveal relationships between climate change and mental health. We developed a semantic web tool that uses linked data principles and semantic web technologies to integrate data from multiple sources and analyze them together. We are converting statistical data on climate change and disaster records obtained from the World Bank data catalog and the International Disaster Database into a Resource Description Framework (RDF) representation that was annotated with the RDF Data Cube vocabulary. We compare these data with a dataset of tweets that mention terms from the Emotion Ontology to get a sense of how disasters can impact the affected populations. This dataset is being gathered using an infrastructure we developed that extracts term uses in Twitter with controlled vocabularies. This data was also converted to RDF structure so that statistical data on the climate change and disasters is analyzed together with sentiment data. To visualize and explore relationship of the multiple data across the dimensions of time and location, we use the qb.js framework. We are using this approach to investigate the social and emotional impact of climate change. We hope that this will demonstrate the use of social media data as a valuable source of understanding on global climate change.

Sentiment analysis and the impact of employee satisfaction on firm earnings

NARCIS (Netherlands)

Moniz, Andy; de Jong, Franciska M.G.

Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder

Classifying emotion in Twitter using Bayesian network

Science.gov (United States)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Sentiment analysis on tweets for social events

DEFF Research Database (Denmark)

Zhou, Xujuan; Tao, Xiaohui; Yong, Jianming

2013-01-01

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event......, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets...... Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific...

Twitter analysis of the orthodontic patient experience with braces vs Invisalign.

Science.gov (United States)

Noll, Daniel; Mahon, Brendan; Shroff, Bhavna; Carrico, Caroline; Lindauer, Steven J

2017-05-01

To examine the orthodontic patient experience having braces compared with Invisalign by means of a large-scale Twitter sentiment analysis. A custom data collection program was created that collected tweets containing the words "braces" or "Invisalign" for a period of 5 months. A hierarchal Naïve Bayes sentiment analysis classifier was developed to sort the tweets into five categories: positive, negative, neutral, advertisement, or not applicable. Each category was then analyzed for specific content. A total of 419,363 tweets applicable to orthodontics were collected. Users posted significantly more positive tweets (61%) than they did negative tweets (39%; P ≤ .0001). There was no significant difference in the distribution of positive and negative sentiment between braces and Invisalign tweets (P = .4189). Positive orthodontics-related tweets often highlighted gratitude for a great smile accompanied with selfies. Negative orthodontic tweets frequently focused on pain. Twitter users expressed more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign tweets.

Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

DEFF Research Database (Denmark)

Felbo, Bjarke; Mislove, Alan; Søgaard, Anders

2017-01-01

NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more...... diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-theart performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained...... model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches....

Measuring Corporate Reputation using Sentiment Analysis

DEFF Research Database (Denmark)

Colleoni, Elanor; Arvidsson, Adam; Hansen, Lars K.

and monitor reputation through the analysis of user generated content in real-time. In this paper, we show how social media content can be used to measure the online reputation of a company. Furthermore, we present an open platform that uses a sentiment analysis algorithm on twitter traffic to monitor...

An effective convolutional neural network model for Chinese sentiment analysis

Science.gov (United States)

Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

2017-06-01

Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

DATA MINING TWITTER TO PREDICT STOCK MARKET MOVEMENTS

Directory of Open Access Journals (Sweden)

Maxim PECIONCHIN

2015-04-01

Full Text Available In this paper we apply sentiment analysis of Twitter data from July through December, 2013 to find correlation between users’ sentiments and NASDAQ closing price and trading volume. Our analysis is based on the Affective Norms for English Words (ANEW. We propose a novel way of determining weighted mood level based on PageRank algorithm. We find that sentiment data is Granger-causal to financial market performance with high degree of significance. “Happy” and “sad” sentiment variables’ lags are strongly correlated with closing price and “excited” and “calm” lags are strongly correlated with trading volume.

Twitter as driver of stock price

OpenAIRE

Jubbega, Annika

2012-01-01

The goal of this research is to examine the dynamic relationship of Twitter and stock price, by examining the effects for the ten most valuable brands according Interbrand (2010): Coca-Cola, IBM, Microsoft, Google, McDonald’s, Intel, Nokia, Disney, Toyota and Cisco. A VAR modelling approach captures the short and long term effects of Twitter to stock price and stock price to Twitter. Effects were found for 5 of the 10 brand. For Coca-Cola and Toyota, the number of brand sentiment tweets dri...

Good Friends, Bad News - Affect and Virality in Twitter

DEFF Research Database (Denmark)

Hansen, Lars Kai; Arvidsson, Adam; Nielsen, Finn Årup

2011-01-01

The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. The basic measure of virality in Twitter is the prob......The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. The basic measure of virality in Twitter...

Semantic network analysis of vaccine sentiment in online social media.

Science.gov (United States)

Kang, Gloria J; Ewing-Nelson, Sinclair R; Mackey, Lauren; Schlitt, James T; Marathe, Achla; Abbas, Kaja M; Swarup, Samarth

2017-06-22

To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines. Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood. We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment. The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits. Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage

Public Response to Obamacare on Twitter.

Science.gov (United States)

Davis, Matthew A; Zheng, Kai; Liu, Yang; Levy, Helen

2017-05-26

The Affordable Care Act (ACA), often called "Obamacare," is a controversial law that has been implemented gradually since its enactment in 2010. Polls have consistently shown that public opinion of the ACA is quite negative. The aim of our study was to examine the extent to which Twitter data can be used to measure public opinion of the ACA over time. We prospectively collected a 10% random sample of daily tweets (approximately 52 million since July 2011) using Twitter's streaming application programming interface (API) from July 10, 2011 to July 31, 2015. Using a list of key terms and ACA-specific hashtags, we identified tweets about the ACA and examined the overall volume of tweets about the ACA in relation to key ACA events. We applied standard text sentiment analysis to assign each ACA tweet a measure of positivity or negativity and compared overall sentiment from Twitter with results from the Kaiser Family Foundation health tracking poll. Public opinion on Twitter (measured via sentiment analysis) was slightly more favorable than public opinion measured by the Kaiser poll (approximately 50% vs 40%, respectively) but trends over time in both favorable and unfavorable views were similar in both sources. The Twitter-based measures of opinion as well as the Kaiser poll changed very little over time: correlation coefficients for favorable and unfavorable public opinion were .43 and .37, respectively. However, we found substantial spikes in the volume of ACA-related tweets in response to key events in the law's implementation, such as the first open enrollment period in October 2013 and the Supreme Court decision in June 2012. Twitter may be useful for tracking public opinion of health care reform as it appears to be comparable with conventional polling results. Moreover, in contrast with conventional polling, the overall amount of tweets also provides a potential indication of public interest of a particular issue at any point in time. ©Matthew A Davis, Kai Zheng

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Science.gov (United States)

Kern, Margaret L; Fulcher, Ben D; Rickard, Nikki S

2018-01-01

Background Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. Objective The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. Methods Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. Results Negative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=−.34, P=.01, 95% CI −0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. Conclusions The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform. PMID:29739736

Is humility a sentiment?

Science.gov (United States)

Weidman, Aaron C; Tracy, Jessica L

2017-01-01

Gervais & Fessler reintroduce the concept of a sentiment as a framework for conceptualizing contempt, a construct with both attitudinal and emotional components. We propose that humility might also fit this mold. We review recent findings regarding the antecedents, phenomenology, and functional consequences of humility, and discuss why conceptualizing it as a sentiment may advance our understanding of this construct.

Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study.

Science.gov (United States)

Metwally, Omar; Blumberg, Seth; Ladabaum, Uri; Sinha, Sidhartha R

2017-06-07

Although cancer screening reduces morbidity and mortality, millions of people worldwide remain unscreened. Social media provide a unique platform to understand public sentiment toward tools that are commonly used for cancer screening. The objective of our study was to examine public sentiment toward colonoscopy, mammography, and Pap smear and how this sentiment spreads by analyzing discourse on Twitter. In this observational study, we classified 32,847 tweets (online postings on Twitter) related to colonoscopy, mammography, or Pap smears using a naive Bayes algorithm as containing positive, negative, or neutral sentiment. Additionally, we characterized the spread of sentiment on Twitter using an established model to study contagion. Colonoscopy-related tweets were more likely to express negative than positive sentiment (negative to positive ratio 1.65, 95% CI 1.51-1.80, Psocial media data provides a unique, quantitative framework to better understand the public's perception of medical interventions that are commonly used for cancer screening. Given the growing use of social media, public health interventions to improve cancer screening should use the health perceptions of the population as expressed in social network postings about tests that are frequently used for cancer screening, as well as other people they may influence with such postings. ©Omar Metwally, Seth Blumberg, Uri Ladabaum, Sidhartha R. Sinha. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.06.2017.

Weibo sentiments and stock return: A time-frequency view.

Science.gov (United States)

Xu, Yingying; Liu, Zhixin; Zhao, Jichang; Su, Chiwei

2017-01-01

This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter's variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which have both frequency and time-varying features. Five detailed sentiments are positively related to the stock return for certain periods, particularly since October 2014 at medium to high frequencies of less than ten trading days, when the stock return is undergoing significant fluctuations. Sadness appears to have a closer relationship with the stock return than the other four sentiments. As to the lead-lag relationships, the stock return causes Weibo sentiments rather than reverse for most of the periods with significant linkages. Compared with polarity sentiments (negative vs. positive), detailed sentiments provide more information regarding relationships between Weibo sentiments and the stock market. The stock market exerts positive effects on bullishness and agreement of microblogs. Meanwhile, agreement leads the stock return in-phase at the frequency of approximately 40 trading days, indicating that less disagreement improves certainty about the stock market.

Weibo sentiments and stock return: A time-frequency view.

Directory of Open Access Journals (Sweden)

Yingying Xu

Full Text Available This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter's variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which have both frequency and time-varying features. Five detailed sentiments are positively related to the stock return for certain periods, particularly since October 2014 at medium to high frequencies of less than ten trading days, when the stock return is undergoing significant fluctuations. Sadness appears to have a closer relationship with the stock return than the other four sentiments. As to the lead-lag relationships, the stock return causes Weibo sentiments rather than reverse for most of the periods with significant linkages. Compared with polarity sentiments (negative vs. positive, detailed sentiments provide more information regarding relationships between Weibo sentiments and the stock market. The stock market exerts positive effects on bullishness and agreement of microblogs. Meanwhile, agreement leads the stock return in-phase at the frequency of approximately 40 trading days, indicating that less disagreement improves certainty about the stock market.

Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns

Science.gov (United States)

Shen, Dehua; Liu, Lanbiao; Zhang, Yongjie

2018-01-01

The constantly increasing utilization of social media as the alternative information channel, e.g., Twitter, provides us a unique opportunity to investigate the dynamics of the financial market. In this paper, we employ the daily happiness sentiment extracted from Twitter as the proxy for the online sentiment dynamics and investigate its association with the skewness of stock returns of 26 international stock market index returns. The empirical results show that: (1) by dividing the daily happiness sentiment into quintiles from the least to the most happiness days, the skewness of the Most-happiness subgroup is significantly larger than that of the Least-happiness subgroup. Besides, there exist significant differences in any pair of subgroups; (2) in an event study methodology, we further show that the skewness around the highest happiness days is significantly larger than the skewness around the lowest happiness days.

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates.

Science.gov (United States)

Seabrook, Elizabeth M; Kern, Margaret L; Fulcher, Ben D; Rickard, Nikki S

2018-05-08

Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. Negative emotion word instability was a significant predictor of greater depression severity on Facebook (r s (29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial r s (26)=.51, P=.006) and within-person variability (partial r s (26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (r s (49)=-.34, P=.01, 95% CI -0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform. ©Elizabeth M Seabrook, Margaret L Kern, Ben D Fulcher, Nikki S Rickard. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.05.2018.

Sentiment Analysis and Opinion Mining

CERN Document Server

Liu, Bing

2012-01-01

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions

Affective Computing and Sentiment Analysis

CERN Document Server

Ahmad, Khurshid

2011-01-01

This volume maps the watershed areas between two 'holy grails' of computer science: the identification and interpretation of affect -- including sentiment and mood. The expression of sentiment and mood involves the use of metaphors, especially in emotive situations. Affect computing is rooted in hermeneutics, philosophy, political science and sociology, and is now a key area of research in computer science. The 24/7 news sites and blogs facilitate the expression and shaping of opinion locally and globally. Sentiment analysis, based on text and data mining, is being used in the looking at news

Sentiment Polarity Analysis based multi-dictionary

Science.gov (United States)

Jiao, Jian; Zhou, Yanquan

This paper presents a novel algorithm for Chinese online reviews, which identifies sentiment polarity. To determine the sentence is negative or positive, we extracted opinion words and identified their opinion targets by CRFs and establish the absolute emotional dictionary (AbED), the relative emotional dictionary (ReED), the field of emotional dictionary (FiED) and the field of targets and opinion words dictionary (TfED). With those emotional dictionary, negative dictionary and modified dictionary, we achieved an effective algorithm to discriminate sentiment polarity by multi-string pattern matching algorithm. For evaluation, we used car online reviews, hotel online reviews and computer online reviews which annotated positive or negative. Experimental results show that our proposed method has made a higher precision and recall rate.

Sentiment analysis and ontology engineering an environment of computational intelligence

CERN Document Server

Chen, Shyi-Ming

2016-01-01

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applica...

Astrophysicists' conversational connections on Twitter.

Directory of Open Access Journals (Sweden)

Kim Holmberg

Full Text Available Because Twitter and other social media are increasingly used for analyses based on altmetrics, this research sought to understand what contexts, affordance use, and social activities influence the tweeting behavior of astrophysicists. Thus, the presented study has been guided by three research questions that consider the influence of astrophysicists' activities (i.e., publishing and tweeting frequency and of their tweet construction and affordance use (i.e. use of hashtags, language, and emotions on the conversational connections they have on Twitter. We found that astrophysicists communicate with a variety of user types (e.g. colleagues, science communicators, other researchers, and educators and that in the ego networks of the astrophysicists clear groups consisting of users with different professional roles can be distinguished. Interestingly, the analysis of noun phrases and hashtags showed that when the astrophysicists address the different groups of very different professional composition they use very similar terminology, but that they do not talk to each other (i.e. mentioning other user names in tweets. The results also showed that in those areas of the ego networks that tweeted more the sentiment of the tweets tended to be closer to neutral, connecting frequent tweeting with information sharing activities rather than conversations or expressing opinions.

LONG CHAINS OR STABLE COMMUNITIES? THE ROLE OF EMOTIONAL STABILITY IN TWITTER CONVERSATIONS

DEFF Research Database (Denmark)

Celli, Fabio; Rossi, Luca

2014-01-01

In this article, we address the issue of how emotional stability affects social relationships in Twitter. In particular, we focus our study on users’ communicative interactions, identified by the symbol “@.” We collected a corpus of about 200,000 Twitter posts, and we annotated it with our...... for the analysis of Twitter data. Social network analysis shows that, whereas secure users have more mutual connections, neurotic users post more than secure ones and have the tendency to build longer chains of interacting users. Clustering coefficient analysis reveals that, whereas secure users tend to build...

Portrayal of waterpipe (shisha, hookah, nargile) smoking on Twitter: a qualitative exploration.

Science.gov (United States)

Grant, A; O'Mahoney, H

2016-11-01

To describe and characterize social media content in relation to waterpipe smoking using qualitative methods. Exploratory qualitative design. A representative sample of pre-existing social media content from Twitter relating to waterpipe smoking and written in the English language was collected during a 1 week period in July 2014. A total of 9671 tweets were collected; duplicates and retweets were removed leaving 4439 unique tweets. Data were analyzed semiotically (positive, negative, positive and negative, no sentiment, unclassifiable) and thematically. Photographs attached to tweets written by individual users indexed using #hookah (n = 299) were subjected to content analysis. Over half of all tweets were positive about waterpipe smoking (59%), with 3% negative, 21% lacking sentiment and 17% unclassifiable. However, there were variations by likely author of tweet, with 91% of tweets from individual users classified as positive. Twitter users focused on their emotional experience, location, other products they were consuming alongside waterpipe smoking, and who they were with. Analysis of photographs highlighted a high degree of synergy between text and visual representations of waterpipe smoking, and two thirds of photographs contained at least part of a waterpipe. Waterpipe smoking may be normalized as an enjoyable activity in this online environment, posing a challenge for public health. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Semantic Sentiment Analysis of Twitter Data

OpenAIRE

Nakov, Preslav

2017-01-01

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that...

DEEP LEARNING MODEL FOR BILINGUAL SENTIMENT CLASSIFICATION OF SHORT TEXTS

Directory of Open Access Journals (Sweden)

Y. B. Abdullin

2017-01-01

Full Text Available Sentiment analysis of short texts such as Twitter messages and comments in news portals is challenging due to the lack of contextual information. We propose a deep neural network model that uses bilingual word embeddings to effectively solve sentiment classification problem for a given pair of languages. We apply our approach to two corpora of two different language pairs: English-Russian and Russian-Kazakh. We show how to train a classifier in one language and predict in another. Our approach achieves 73% accuracy for English and 74% accuracy for Russian. For Kazakh sentiment analysis, we propose a baseline method, that achieves 60% accuracy; and a method to learn bilingual embeddings from a large unlabeled corpus using a bilingual word pairs.

Characterizing Sleep Issues Using Twitter.

Science.gov (United States)

McIver, David J; Hawkins, Jared B; Chunara, Rumi; Chatterjee, Arnaub K; Bhandari, Aman; Fitzgerald, Timothy P; Jain, Sachin H; Brownstein, John S

2015-06-08

Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (Pcost-effective, and customizable data to be gathered.

Emotional Indoctrination through Sentimental Narrative in Spanish Primary Education Textbooks during the Franco Dictatorship (1939-1959)

Science.gov (United States)

Mahamud, Kira

2016-01-01

This paper aims to highlight the prominence and relevance attached by the Franco dictatorial regime to emotions and sentiments in primary education textbooks. The authors of school textbooks employed a singular writing style, which enabled them to permeate the regime's ideology within the primary education community and classroom. Overcoming the…

Semantic Sentiment Analysis in Arabic Social Media

Directory of Open Access Journals (Sweden)

Samir Tartir

2017-04-01

Full Text Available Social media is a huge source of information. And is increasingly being used by governments, companies, and marketers to understand how the crowd thinks. Sentiment analysis aims to determine the attitudes of a group of people that are using one or more social media platforms with respect to a certain topic. In this paper, we propose a semantic approach to discover user attitudes and business insights from social media in Arabic, both standard and dialects. We also introduce the first version of our Arabic Sentiment Ontology (ASO that contains different words that express feelings and how strongly these words express these feelings. We then show the usability of our approach in classifying different Twitter feeds on different topics.

Twitter analytics as indicator of news engagement

NARCIS (Netherlands)

Vergeer, M.R.M.; Skoric, M.M.; Parycek, P.; Sachs, M.

2017-01-01

The rise of popularity of social media (e.g. Facebook, Twitter, Plurk, Mixi) to share opinions about what's on people's mind has opened possibilities to track the public's activities and sentiments. By using generic identifiers (hashtags #journaal and #RTLnieuws) on two of the most watched news

Impact of Family Sentiments on Indian Woman and Their Buying Behaviour

OpenAIRE

Ramesh Babu Kakumanu; Dr. V. Israel Raju; Dr. A. Kishore Kumar

2015-01-01

A woman basically is an emotional creature and woman without emotions is like cake without cream. They undergo all types of emotions like joy, fear, anger, humor, sex, empathy and love. Family sentiments are most powerful tools to push woman into emotional state of mind. Family sentiments in the form of relationships, sharing love, caring, security, warmth, sex, and living together are frequently used in the advertisements. This article studies family appeals impact on Indian woman. The age b...

Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams

Science.gov (United States)

Hao, Ming; Rohrdantz, Christian; Janetzko, Halldór; Keim, Daniel; Dayal, Umeshwar; Haug, Lars-Erik; Hsu, Mei-Chun

2012-01-01

Twitter currently receives over 190 million tweets (small text-based Web posts) and manufacturing companies receive over 10 thousand web product surveys a day, in which people share their thoughts regarding a wide range of products and their features. A large number of tweets and customer surveys include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for determining customer sentiments. To explore high-volume customer feedback streams, we integrate three time series-based visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel idea of term associations that identify attributes, verbs, and adjectives frequently occurring together; and (3) new pixel cell-based sentiment calendars, geo-temporal map visualizations and self-organizing maps to identify co-occurring and influential opinions. We have combined these techniques into a well-fitted solution for an effective analysis of large customer feedback streams such as for movie reviews (e.g., Kung-Fu Panda) or web surveys (buyers).

Vliv nálady na sociální síti Twitter na kurz akciových titulů

OpenAIRE

Fiala, Vojtěch

2015-01-01

This diploma thesis deals with a question of identification of causality between sentiment on social network Twitter and a price of specific, publicly traded stocks on New York Stock Exchange (NYSE). By a multi criteria analysis were chosen stocks of Microsoft Corporation and Apple Inc. There is constructed a model, which identifies authors messages on Twitter -- tweets and sentiment which they carry in relation to companies. Success of this model is examined by both qualitative and quantitat...

Opinion Analysis on Rohingya using Twitter Data

Science.gov (United States)

Rochmawati, N.; Wibawa, S. C.

2018-04-01

Rohingya is an ethnicity in Myanmar. Recently there was a conflict in the area between the Rakhine population and the Myanmar army. Many opinions are pro and contra in addressing this issue. There is a critic, there is a support and there is a neutral. The purpose of this paper is to analyze the world public opinion about the case of Rohingya. The opinion data to be processed is taken from twitter. the reason for using twitter is because twitter has become one of the popular social media and includes the most frequently visited social media. Therefore, it would be a lot of data that can be taken from twitter to be processed in the process of sentiment analysis. The grouping of opinions will be divided into 3 parts of positive, negative and neutral. the method used in grouping is the naïve Bayes method.

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

Energy Technology Data Exchange (ETDEWEB)

Steed, Chad A [ORNL; Drouhard, Margaret MEG G [ORNL; Beaver, Justin M [ORNL; Pyle, Joshua M [ORNL; BogenII, Paul L. [Google Inc.

2015-01-01

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction, Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.

Sentiment Propagation in Social Networks: A Case Study in LiveJournal

Science.gov (United States)

Zafarani, Reza; Cole, William D.; Liu, Huan

Social networking websites have facilitated a new style of communication through blogs, instant messaging, and various other techniques. Through collaboration, millions of users participate in millions of discussions every day. However, it is still difficult to determine the extent to which such discussions affect the emotions of the participants. We surmise that emotionally-oriented discussions may affect a given user's general emotional bent and be reflected in other discussions he or she may initiate or participate in. It is in this way that emotion (or sentiment) may propagate through a network. In this paper, we analyze sentiment propagation in social networks, review the importance and challenges of such a study, and provide methodologies for measuring this kind of propagation. A case study has been conducted on a large dataset gathered from the LiveJournal social network. Experimental results are promising in revealing some aspects of the sentiment propagation taking place in social networks.

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter.

Science.gov (United States)

Massey, Philip M; Leader, Amy; Yom-Tov, Elad; Budenz, Alexandra; Fisher, Kara; Klassen, Ann C

2016-12-05

Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities. The objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data. We collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140-character tweet text most associated with each keyword. We used chi-square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment. A total of 193,379 English-language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with "HPV vaccine" and more negative words associated with name-brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99% of the sample), followed by negative sentiment with 48,940 tweets (25.31% of the sample). Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

KAUST Repository

Salas-Zárate, María del Pilar

2017-02-19

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

KAUST Repository

Salas-Zá rate, Marí a del Pilar; Medina-Moreira, José ; Lagos-Ortiz, Katty; Luna-Aveiga, Harry; Rodriguez-Garcia, Miguel Angel; Valencia-Garcí a, Rafael

2017-01-01

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter.

Science.gov (United States)

Clark, Eric M; Jones, Chris A; Williams, Jake Ryland; Kurti, Allison N; Norotsky, Mitchell Craig; Danforth, Christopher M; Dodds, Peter Sheridan

2016-01-01

Twitter has become the "wild-west" of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, "kid-friendly" flavors, algorithmically generated false testimonials, and free samples. All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words 'ban', 'tobacco', 'doesn't', 'drug', 'against', 'poison', 'tax' and a relative decrease in the positive words like 'haha', 'good', 'cool'. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like 'best', 'win', 'buy', 'sale', 'health', 'discount' and a relative decrease in negative words like 'bad', 'hate', 'stupid', 'don't'. Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing.

Choosing your weapons : on sentiment analysis tools for software engineering research

NARCIS (Netherlands)

Jongeling, R.M.; Datta, S.; Serebrenik, A.; Koschke, R.; Krinke, J.; Robillard, M.

2015-01-01

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these

Querying Sentiment Development over Time

DEFF Research Database (Denmark)

Andreasen, Troels; Christiansen, Henning; Have, Christian Theil

2013-01-01

A new language is introduced for describing hypotheses about fluctuations of measurable properties in streams of timestamped data, and as prime example, we consider trends of emotions in the constantly flowing stream of Twitter messages. The language, called EmoEpisodes, has a precise semantics...... that measures how well a hypothesis characterizes a given time interval; the semantics is parameterized so it can be adjusted to different views of the data. EmoEpisodes is extended to a query language with variables standing for unknown topics and emotions, and the query-answering mechanism will return...... instantiations for topics and emotions as well as time intervals that provide the largest deflections in this measurement. Experiments are performed on a selection of Twitter data to demonstrates the usefulness of the approach....

Using Sentiment Analysis to Observe How Science is Communicated

Science.gov (United States)

Topping, David; Illingworth, Sam

2016-04-01

'Citizen Science' and 'Big data' are terms that are currently ubiquitous in the field of science communication. Whilst opinions differ as to what exactly constitutes a 'citizen', and how much information is needed in order for a data set to be considered truly 'big', what is apparent is that both of these fields have the potential to help revolutionise not just the way that science is communicated, but also the way that it is conducted. However, both the generation of sufficient data, and the efficiency of then analysing the data once it has been analysed need to be taken into account. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. The process of sentiment analysis can be automated, providing that an adequate training set has been used, and that the nuances that are associated with a particular topic have been accounted for. Given the large amounts of data that are generated by social media posts, and the often-opinionated nature of these posts, they present an ideal source of data to both train with and then scrutinize using sentiment analysis. In this work we will demonstrate how sentiment analysis can be used to examine a large number of Twitter posts, and how a training set can be established to ensure consistency and accuracy in the automation. Following an explanation of the process, we will demonstrate how automated sentiment analysis can be used to categorise opinions in relation to a large-scale science festival, and will discuss if sentiment analysis can be used to tell us if there is a bias in these communications. We will also investigate if sentiment analysis can be used to replace more traditional, and invasive evaluation strategies, and how this approach can then be adopted to investigate other topics, both within scientific communication and in the wider scientific context.

Detecting tweet-based sentiment polarity of plastic surgery treatment

International Nuclear Information System (INIS)

Jokhio, M.; Mahoto, N.A.

2015-01-01

Sentiment analysis is a growing research these days. Many companies perform this analysis on public opinions to get a general idea about any product or service. This paper presents a novel approach to get views or comments of Twitter users about plastic surgery treatments. The proposed approach uses machine-learning technique embedded with the naive Bayesian classifier to assign polarities (i.e. positive, negative or neutral) to the tweets, collected from Twitter micro-blogging website. The accuracy of the obtained results has been validated using precision, recall and F-score measures. It has been observed from 25000 tweets dataset that people tend to have positive as well as substantial negative opinions regarding particular treatments. The experimental results show the effectiveness of the proposed approach. (author)

Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity

Directory of Open Access Journals (Sweden)

Devid Haryalesmana Wahid

2016-07-01

Full Text Available The using of Twitter by selebrities has become a new trend of impression management strategy. Mining public reaction in social media is a good strategy to obtain feedbacks, but extracting it are not trivial matter. Reads hundred of tweets while determine their sentiment polarity are time consuming. Extractive sentiment summarization machine are needed to address this issue. Previous research generally do not include sentiment information contained in a tweet as weight factor, as a results only general topics of discussion are extracted. This research aimed to do an extractive sentiment summarization on both positive and negative sentiment mentioning Indonesian selebrity, Agnes Monica, by combining SentiStrength, Hybrid TF-IDF, and Cosine Similarity. SentiStrength is used to obtain sentiment strength score and classify tweet as a positive, negative or neutral. The summarization of posisitve and negative sentiment can be done by rank tweets using Hybrid TF-IDF summarization and sentiment strength score as additional weight then removing similar tweet by using Cosine Similarity. The test results showed that the combination of SentiStrength, Hybrid TF-IDF, and Cosine Similarity perform better than using Hybrid TF-IDF only, given an average 60% accuracy and 62% f-measure. This is due to the addition of sentiment score as a weight factor in sentiment summ­ari­zation.

USER EMOTION IDENTIFICATION IN TWITTER USING SPECIFIC FEATURES: HASHTAG, EMOJI, EMOTICON, AND ADJECTIVE TERM

Directory of Open Access Journals (Sweden)

Yuita Arum Sari

2014-08-01

Full Text Available Abstract Twitter is a social media application, which can give a sign for identifying user emotion. Identification of user emotion can be utilized in commercial domain, health, politic, and security problems. The problem of emotion identification in twit is the unstructured short text messages which lead the difficulty to figure out main features. In this paper, we propose a new framework for identifying the tendency of user emotions using specific features, i.e. hashtag, emoji, emoticon, and adjective term. Preprocessing is applied in the first phase, and then user emotions are identified by means of classification method using kNN. The proposed method can achieve good results, near ground truth, with accuracy of 92%.

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter.

Directory of Open Access Journals (Sweden)

Eric M Clark

Full Text Available Twitter has become the "wild-west" of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, "kid-friendly" flavors, algorithmically generated false testimonials, and free samples.All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching.The overwhelming majority (80% of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013, up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014 due to a relative increase in the negative words 'ban', 'tobacco', 'doesn't', 'drug', 'against', 'poison', 'tax' and a relative decrease in the positive words like 'haha', 'good', 'cool'. Automated tweets are more positive than organic (6.17 versus 5.84 due to a relative increase in the marketing words like 'best', 'win', 'buy', 'sale', 'health', 'discount' and a relative decrease in negative words like 'bad', 'hate', 'stupid', 'don't'.Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing.

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

Science.gov (United States)

Jones, Chris A.; Williams, Jake Ryland; Kurti, Allison N.; Norotsky, Mitchell Craig; Danforth, Christopher M.; Dodds, Peter Sheridan

2016-01-01

Background Twitter has become the “wild-west” of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, “kid-friendly” flavors, algorithmically generated false testimonials, and free samples. Methods All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. Results The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words ‘ban’, ‘tobacco’, ‘doesn’t’, ‘drug’, ‘against’, ‘poison’, ‘tax’ and a relative decrease in the positive words like ‘haha’, ‘good’, ‘cool’. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like ‘best’, ‘win’, ‘buy’, ‘sale’, ‘health’, ‘discount’ and a relative decrease in negative words like ‘bad’, ‘hate’, ‘stupid’, ‘don’t’. Conclusions Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media

Tweeting the meeting: an in-depth analysis of Twitter activity at Kidney Week 2011.

Directory of Open Access Journals (Sweden)

Tejas Desai

Full Text Available In recent years, the American Society of Nephrology (ASN has increased its efforts to use its annual conference to inform and educate the public about kidney disease. Social media, including Twitter, has been one method used by the Society to accomplish this goal. Twitter is a popular microblogging service that serves as a potent tool for disseminating information. It allows for short messages (140 characters to be composed by any author and distributes those messages globally and quickly. The dissemination of information is necessary if Twitter is to be considered a tool that can increase public awareness of kidney disease. We hypothesized that content, citation, and sentiment analyses of tweets generated from Kidney Week 2011 would reveal a large number of educational tweets that were disseminated to the public. An ideal tweet for accomplishing this goal would include three key features: 1 informative content, 2 internal citations, and 3 positive sentiment score. Informative content was found in 29% of messages, greater than that found in a similarly sized medical conference (2011 ADA Conference, 16%. Informative tweets were more likely to be internally, rather than externally, cited (38% versus 22%, p<0.0001, thereby amplifying the original information to an even larger audience. Informative tweets had more negative sentiment scores than uninformative tweets (means -0.162 versus 0.199 respectively, p<0.0001, therefore amplifying a tweet whose content had a negative tone. Our investigation highlights significant areas of promise and improvement in using Twitter to disseminate medical information in nephrology from a scientific conference. This goal is pertinent to many nephrology-focused conferences that wish to increase public awareness of kidney disease.

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Directory of Open Access Journals (Sweden)

María del Pilar Salas-Zárate

2017-01-01

Full Text Available In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others. Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around. To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.

Network public opinion space sentiment tendency analyze based on recurrent convolution neural network

Science.gov (United States)

Zhang, Gaowei; Xu, Lingyu; Wang, Lei

2018-04-01

The purpose of this chapter is to analyze the investor's psychological characteristics and investment decision-making behavior characteristics, to study the investor sentiment under the network public opinion, and then analyze from three aspects: First, investor sentiment analysis and how to spread in the online media; The influence mechanism of investor's emotion on the stock market and its effect; the third one is to measure the investor's emotion based on the degree of attention, trying hard to sort out the internal relations between the investor's sentiment and the network public opinion and the stock market, in order to lay the theoretical foundation of this article.

Detecting Tweet-Based Sentiment Polarity of Plastic Surgery Treatment

Directory of Open Access Journals (Sweden)

Marvi Jokhio

2015-10-01

Full Text Available Sentiment analysis is a growing research these days. Many companies perform this analysis on public opinions to get a general idea about any product or service. This paper presents a novel approach to get views or comments of Twitter users about plastic surgery treatments. The proposed approach uses machine-learning technique embedded with the naïve Bayesian classifier to assign polarities (i.e. positive, negative or neutral to the tweets, collected from ?Twitter micro-blogging website?. The accuracy of the obtained results has been validated using precision, recall and F-score measures. It has been observed from 25000 tweets dataset that people tend to have positive as well as substantial negative opinions regarding particular treatments. The experimental results show the effectiveness of the proposed approach

Sentiment topic mining based on comment tags

Science.gov (United States)

Zhang, Daohai; Liu, Xue; Li, Juan; Fan, Mingyue

2018-03-01

With the development of e-commerce, various comments based on tags are generated, how to extract valuable information from these comment tags has become an important content of business management decisions. This study takes HUAWEI mobile phone tags as an example using the sentiment analysis and topic LDA mining method. The first step is data preprocessing and classification of comment tag topic mining. And then make the sentiment classification for comment tags. Finally, mine the comments again and analyze the emotional theme distribution under different sentiment classification. The results show that HUAWEI mobile phone has a good user experience in terms of fluency, cost performance, appearance, etc. Meanwhile, it should pay more attention to independent research and development, product design and development. In addition, battery and speed performance should be enhanced.

Public sentiment and movement patterns during natural hazards

Science.gov (United States)

Huang, Q.; Guo, C.

2017-12-01

Recently, we have unfortunately witnessed a series of deadly hurricane events, including Harvey, Irma, Jose, Maria and Nate. Effective disaster management actions such as getting citizen sheltered and evacuated can significantly reduce injuries and fatalities. A more comprehensive understanding of citizen's perceptions (e.g., sentiment) about a disaster and movement behaviors (e.g., evacuation) will help improve disaster management and decision making during natural hazards. With the popularity of various social media platforms (i.e. Twitter), there has been great potentials in using social media data to detect and analyze citizen's perceptions and moving behaviors before, during and after a natural hazard. Using the geo-tagged tweets generated during recent hurricane events, the study will examine the movement interactions between citizens and a hurricane and also explore citizens' tweeting behaviors and sentiments at different stages of a disaster. The results provide insights on understanding 1) spatiotemporal patterns of public movements (i.e., when and where did people's movements happen), 2) how were people's movements related to the hurricane trajectory, 3) when did people in different locations start to pay attention to the hurricane, and finally 4) how were the sentiments of people in different places towards the hurricane during different disaster stages.

Social Robots, fiction, and sentimentality

DEFF Research Database (Denmark)

Rodogno, Raffaele

2016-01-01

in the philosophy of art and in cognitive science that attempts to solve the so called paradox of fictional emotions, i.e., the seemingly paradoxical way in which we respond emotionally to fictional or imaginary characters and events. If sentimentality were not at issue, neither would its immorality. For the sake...... argue that there are other reasons to be worried about the wide-spread use of ersatz companionship technology that have to do with the potential loss of valuable, self-defining forms of life....

Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis

Directory of Open Access Journals (Sweden)

Guillem Aguado

2018-05-01

Full Text Available The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs, and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a user having an affective state considered negative by an Analyzer can affect other users and generate bad repercussions. Those Analyzers that we propose are a Sentiment Analyzer, a Stress Analyzer and a novel combined Analyzer. We also want to discover what Analyzer is more suitable to predict a bad future situation, and in what context. We designed a Multi-Agent System (MAS that uses different Analyzers to protect or advise users. This MAS uses the trained and tested Analyzers to predict future bad situations in social media, which could be triggered by the actions of a user that has an emotional state considered negative. We conducted an experimentation with different datasets of text messages from Twitter.com to examine the ability of the system to predict bad repercussions, by comparing the polarity, stress level or combined value classification of the messages that are replies to the ones of the messages that originated them.

Entity-based Classification of Twitter Messages

OpenAIRE

Yerva, Surender Reddy; Miklós, Zoltán; Aberer, Karl

2012-01-01

Twitter is a popular micro-blogging service on theWeb, where people can enter short messages, which then become visible to some other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about some companies or their products. These messages are a rich source of information for companies for sentiment analysis or opinion mining. There is however a great obstacle for analyzing the messages directly: as the company n...

Towards sentiment analysis application in housing projects

Science.gov (United States)

Mahadzir, Nurul Husna; Omar, Mohd Faizal; Nawi, Mohd Nasrun Mohd

2016-08-01

In becoming a develop nation by 2020, Malaysia Government realized the need in providing affordable house to the public. Since Second Malaysia Plan, government has implemented various affordable housing projects and it continues until recent Malaysia Plan. To measure the effectiveness of the initiatives taken, public opinion is necessary. A social media platform has been seen as the most effective mechanism to get information on people's thought and feeling towards certain issues. One of the best ways to extract emotions and thoughts from what people post in social media is through Sentiment Analysis (SA). There are three different levels of analysis: document level, sentence level and feature level. Most of previous research focused on the classification of sentiment at document or sentence level. Unfortunately, both document and sentence level does not discover what exactly people like or not. While the analysis based on feature, there exist accuracy problem when classifying the sentiment scores. This paper will propose a new framework that focuses on sentiment classification scores at feature level to overcome the uncertainty and accuracy issues on the result.

Sentiment and Vision in Charles Dickens's A Christmas Carol and The Cricket on the Hearth

Directory of Open Access Journals (Sweden)

Heather Tilley

2007-04-01

Full Text Available This essay explores the ways in which sentimentality is manifested through the visible, and through associative functions of the eye, in two of Dickens's Christmas books of the 1840s. I situate the relationship between vision and sentiment within discourses from eighteenth-century moral philosophy, as Adam Smith's figure of the “Impartial Spectator” (of central importance to the development of ideas around sympathy is constructed mainly through the visual. I focus on two of the Christmas Books as they offer an interesting local study to test these ideas, coming at a critical juncture within the development of Dickens's own writing style, and also at an important historical moment within an investigation into Victorian sentimentality. Within Dickens's writing, sentimentality is typically associated with the exaggerated emotional portrayal of pathetic scenes (particularly the deaths of children, designed to elicit emotional responses from the reader. However, behind the hyperbole rests a concern with the self's need to take social, ethical and moral care of others, and the role of literature and art in tutoring the reader's emotional response, with the eye playing a crucial role in this act. I further explore the way in which Dickens's interest in blindness both reinforces, and points to certain disturbances, in his sentimental vision.

Facebook and Twitter vaccine sentiment in response to measles outbreaks.

Science.gov (United States)

Deiner, Michael S; Fathy, Cherie; Kim, Jessica; Niemeyer, Katherine; Ramirez, David; Ackley, Sarah F; Liu, Fengchen; Lietman, Thomas M; Porco, Travis C

2017-11-01

Social media posts regarding measles vaccination were classified as pro-vaccination, expressing vaccine hesitancy, uncertain, or irrelevant. Spearman correlations with Centers for Disease Control and Prevention-reported measles cases and differenced smoothed cumulative case counts over this period were reported (using time series bootstrap confidence intervals). A total of 58,078 Facebook posts and 82,993 tweets were identified from 4 January 2009 to 27 August 2016. Pro-vaccination posts were correlated with the US weekly reported cases (Facebook: Spearman correlation 0.22 (95% confidence interval: 0.09 to 0.34), Twitter: 0.21 (95% confidence interval: 0.06 to 0.34)). Vaccine-hesitant posts, however, were uncorrelated with measles cases in the United States (Facebook: 0.01 (95% confidence interval: -0.13 to 0.14), Twitter: 0.0011 (95% confidence interval: -0.12 to 0.12)). These findings may result from more consistent social media engagement by individuals expressing vaccine hesitancy, contrasted with media- or event-driven episodic interest on the part of individuals favoring current policy.

The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place.

Directory of Open Access Journals (Sweden)

Lewis Mitchell

Full Text Available We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1 a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2 annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.

The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place.

Science.gov (United States)

Mitchell, Lewis; Frank, Morgan R; Harris, Kameron Decker; Dodds, Peter Sheridan; Danforth, Christopher M

2013-01-01

We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.

The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place

Science.gov (United States)

Mitchell, Lewis; Frank, Morgan R.; Harris, Kameron Decker; Dodds, Peter Sheridan; Danforth, Christopher M.

2013-01-01

We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates. PMID:23734200

Using Twitter to Understand Public Perceptions Regarding the #HPV Vaccine: Opportunities for Public Health Nurses to Engage in Social Marketing.

Science.gov (United States)

Keim-Malpass, Jessica; Mitchell, Emma M; Sun, Emily; Kennedy, Christine

2017-07-01

Given the degree of public mistrust and provider hesitation regarding the human papillomavirus (HPV) vaccine, it is important to explore how information regarding the vaccine is shared online via social media outlets. The purpose of this study was to evaluate the content of messaging regarding the HPV vaccine on the social media and microblogging site Twitter, and describe the sentiment of those messages. This study utilized a cross-sectional descriptive approach. Over a 2-week period, Twitter content was searched hourly using key terms "#HPV and #Gardasil," which yielded 1,794 Twitter posts for analysis. Each post was then analyzed individually using an a priori coding strategy and directed content analysis. The majority of Twitter posts were written by lay consumers and were sharing commentary about a media source. However, when actual URLs were shared, the most common form of share was linking back to a blog post written by lay users. The vast majority of content was presented as polarizing (either as a positive or negative tweet), with 51% of the Tweets representing a positive viewpoint. Using Twitter to understand public sentiment offers a novel perspective to explore the context of health communication surrounding certain controversial issues. © 2017 Wiley Periodicals, Inc.

"Obesity is the New Major Cause of Cancer": Connections Between Obesity and Cancer on Facebook and Twitter.

Science.gov (United States)

Kent, Erin E; Prestin, Abby; Gaysynsky, Anna; Galica, Kasia; Rinker, Robin; Graff, Kaitlin; Chou, Wen-Ying Sylvia

2016-09-01

Social media interactions can inform public health risk perceptions. While research has examined the risk relationships between obesity and cancer, public attitudes about their associations remain largely unknown. We explored how these constructs were discussed together on two social media platforms. Publicly accessible Facebook and Twitter posts from a 2-month period in 2012 containing references to obesity ("obese/obesity," "overweight," and "fat") and cancer-related words were extracted (N = 3702 posts). Data cleaning yielded a final set of 1382 posts (Facebook: N = 291; Twitter: N = 1091). Using a mixed-methods approach, themes were inductively generated, and sentiment valence, structural elements, and epistemic stance were coded. Seven relational themes emerged: obesity is associated with cancer (n = 389), additional factors are associated with both obesity and cancer (n = 335), obesity causes cancer (n = 85), cancer causes obesity (n = 6), obesity is not linked to cancer (n = 13), co-occurrence (n = 492), and obesity is valued differently than cancer (n = 60). Fifty-nine percent of posts focused on an associative or causal link between obesity and cancer. Thirty-one percent of posts contained positive and/or negative sentiment. Facebook was more likely to contain any sentiment, but Twitter contained proportionately more negative sentiment. Concurrent qualitative analysis revealed a dominance of individual blame for overweight/obese persons and more support and empathy for cancer survivors. Our study reflects wide recognition of the evidence linking obesity to increased risk of cancer, a diverse set of factors perceived to be dually associated with both conditions and differing attribution of responsibility. We demonstrate that social media monitoring can provide an important gauge of public health risk perception.

Using Real-Time Social Media Technologies to Monitor Levels of Perceived Stress and Emotional State in College Students: A Web-Based Questionnaire Study.

Science.gov (United States)

Liu, Sam; Zhu, Miaoqi; Yu, Dong Jin; Rasin, Alexander; Young, Sean D

2017-01-10

College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students. The primary objective of our study was to investigate whether students' perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students' emotional state was associated with the sentiment and emotions of their tweets. We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets. A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Directory of Open Access Journals (Sweden)

Gang Ren

2017-09-01

Full Text Available With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

From Sentiment to Sentimentality: A Nineteenth-Century Lexicographical Search

Directory of Open Access Journals (Sweden)

Marie Banfield

2007-04-01

Full Text Available The brief account of the lexicographical history of the word ‘sentiment' in the nineteenth century, and the table of definitions which follows it, grew from my increasing sense of the shifting and ambivalent nature of the term in the literature of the period, despite the resonance and the proverbial solidity of phrases such as ‘Victorian sentiment' and ‘Victorian sentimentality'. The table is self explanatory, representing the findings of a search, among a wide range of nineteenth-century dictionaries over the period, for the changing meanings accrued by the word ‘sentiment' over time, its extensions and its modifications. The nineteenth-century lexicographical history of the word ‘sentiment' has its chief roots in the Eighteenth-century enlightenment, with definitions from Samuel Johnson and quotations from John Locke, chiefly based on intellect and reason. The nineteenth century generated a number of derivatives of the word over a period of time to express altered modes of feeling, thought and moral concern. The history of the word ‘sentiment' offers a psychological as well as a linguistic narrative.

ANALISIS SENTIMENT PADA SOSIAL MEDIA TWITTER MENGGUNAKAN NAIVE BAYES CLASSIFIER TERHADAP KATA KUNCI “KURIKULUM 2013”

Directory of Open Access Journals (Sweden)

Dyarsa Singgih Pamungkas

2015-11-01

Full Text Available Twitter salah satu situs sosial media yang memungkinkan penggunanya untuk menulis tentang berbagai hal yang terjadi dalam sehari-hari. Banyak pengguna mentweet sebuah produk atau layanan yang mereka gunakan. Tweet tersebut dapat digunakan sebagai sumber data untuk menilai sentimen pada Twitter. Pengguna sering menggunakan singkatan kata dan ejaan kata yang salah, dimana dapat menyulitkan fitur yang diambil serta mengurangi ketepatan klasifikasi. Dalam penelitian ini menggunakan Twitter Search API untuk mengambil data dari twitter, penulis menerapkan proses n-gram karakter untuk seleksi fitur serta menggunakan algoritma Naive Bayes Classifier untuk mengklasifikasi sentimen secara otomatis. Penulis menggunakan 3300 data tweet tentang sentimen kepada kata kunci “kurikulum 2013”. Data tersebut diklasifikasi secara manual dan dibagi kedalam masing-masing 1000 data untuk sentimen positif, negatif dan netral. Untuk proses latih di gunakan 3000 data tweet dan 1000 tweet tiap kategori sentimentnya. Hasil penelitian ini menghasilkan sebuah sistem yang dapat mengklasifikasi sentimen secara otomatis dengan hasil pengujian 3000 data latih dan 100 tweet data ujicoba mencapai 91 %. Kata kunci : Twitter, Twitter Search API, sosial media, tweet, analisis sentimen, sentimen, N-gram, Naive Bayes Classifier.

SAFE: A Sentiment Analysis Framework for E-Learning

Directory of Open Access Journals (Sweden)

Francesco Colace

2014-12-01

Full Text Available The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory.

Psychological Analysis of Jokowi’s First 100 Days and Nawacita from Text in Twitter

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Indro Adinugroho

2018-04-01

Full Text Available In recent days, the public often uses social media such as Twitter for delivering critics; appreciation and campaign related to Government and political issues. The existence of Twitter is changing human behavior rapidly. This study aims to identify Twitter as a medium to generate public opinion concerning two political issues, the 7th Indonesian President first 100 days and public response towards his strategic plan, Nawacita. Method applied in this study is a combination of contemporary research instruments that combines technology and psychology. In this study, the authors examined conversation on Twitter by using Tracker and Algoritma Kata (AK, words algorithm. Tracker is used to collecting conversation on twitter regarding Jokowi’s first 100 days and Nawacita, whereas AK is applied to identify valence and arousal in each tweet collected by Tracker. The finding shows the domination of positive tweets in every week. However, there is a moment where the number of positive tweets was close to negative tweets. In Nawacita issue, law reformation and enforcement was the issue that has highest negative sentiment among others.

Reactions on Twitter to updated alcohol guidelines in the UK: a content analysis.

Science.gov (United States)

Stautz, Kaidy; Bignardi, Giacomo; Hollands, Gareth J; Marteau, Theresa M

2017-02-28

In January 2016, the 4 UK Chief Medical Officers released a public consultation regarding updated guidelines for low-risk alcohol consumption. This study aimed to assess responses to the updated guidelines using comments made on Twitter. Tweets containing the hashtag #alcoholguidelines made during 1 week following the announcement of the updated guidelines were retrieved using the Twitter Archiver tool. The source, sentiment and themes of the tweets were categorised using manual content analysis. A total of 3061 tweets was retrieved. 6 sources were identified, the most prominent being members of the public. Of 821 tweets expressing sentiment specifically towards the guidelines, 80% expressed a negative sentiment. 11 themes were identified, 3 of which were broadly supportive of the guidelines, 7 broadly unsupportive and 1 neutral. Overall, more tweets were unsupportive (49%) than supportive (44%). While the most common theme overall was sharing information, the most common in tweets from members of the public encouraged alcohol consumption (15%) or expressed disagreement with the guidelines (14%), reflecting reactance, resistance and misunderstanding. This descriptive analysis revealed a number of themes present in unsupportive comments towards the updated UK alcohol guidelines among a largely proalcohol community. An understanding of these may help to tailor effective communication of alcohol and health-related policies, and could inform a more dynamic approach to health communication via social media. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.

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Tighe, Patrick J; Goldsmith, Ryan C; Gravenstein, Michael; Bernard, H Russell; Fillingim, Roger B

2015-04-02

Despite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media. The aim was to examine the type, context, and dissemination of pain-related tweets. We used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks. The most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25). Taken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.

National substance use patterns on Twitter.

Directory of Open Access Journals (Sweden)

Hsien-Wen Meng

Full Text Available We examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.A total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.Quality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.The patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.

A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

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Yibo Wang

2018-01-01

Full Text Available Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

Sentiment analysis in twitter data using data analytic techniques for predictive modelling

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Razia Sulthana, A.; Jaithunbi, A. K.; Sai Ramesh, L.

2018-04-01

Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains subjective information and the kind of subjective information it expresses. The subjective information represents the attitude behind the text: positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great concern. We have made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents. We use linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. We conduct a series of experiments to test the performance of the system.

Understanding health food messages on Twitter for health literacy promotion.

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Zhou, J; Liu, F; Zhou, H

2018-05-01

With the popularity of social media, Twitter has become an important tool to promote health literacy. However, many health-related messages on Twitter are dead-ended and cannot reach many people. This is unhelpful for health literacy promotion. This article aims to examine the features of online health food messages that people like to retweet. We adopted rumour theory as our theoretical foundation and extracted seven characteristics (i.e. emotional valence, attractiveness, sender's authoritativeness, external evidence, argument length, hashtags, and direct messages). A total of 10,025 health-related messages on Twitter were collected, and 1496 messages were randomly selected for further analysis. Each message was treated as one unit and then coded. All the hypotheses were tested with logistic regression. Emotional valence, attractiveness, sender's authoritativeness, argument length, and direct messages in a Twitter message had positive effects on people's retweet behaviour. The effect of external evidence was negative. Hashtags had no significant effect after consideration of other variables. Online health food messages containing positive emotions, including pictures, containing direct messages, having an authoritative sender, having longer arguments, or not containing external URLs are more likely to be retweeted. However, a message only containing positive or negative emotions or including direct messages without any support information will not be retweeted.

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter.

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Oscar, Nels; Fox, Pamela A; Croucher, Racheal; Wernick, Riana; Keune, Jessica; Hooker, Karen

2017-09-01

Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset. Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability". This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Social media and palliative medicine: a retrospective 2-year analysis of global Twitter data to evaluate the use of technology to communicate about issues at the end of life.

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Nwosu, Amara Callistus; Debattista, Maria; Rooney, Claire; Mason, Stephen

2015-06-01

Social media describes technological applications which are used to exchange information in a virtual environment. The use of social media is increasing, in professional and social contexts, on a variety of platforms such as Twitter; however, the scope and breadth of its use to discuss end-of-life care has not previously been reported. To determine the frequency, sentiment and trend of Twitter 'tweets' containing palliative care-related identifiers (hashtags) and/or phrases sent by users over a 2-year period. A 2-year retrospective analysis of Twitter posts (tweets), between the 1 August 2011 to 31 July 2013, using a social media analytics tool: TopsyPro. Thirteen search terms were identified and analysed for tweet volume, frequency, sentiment and acceleration. A total of 683.5K tweets containing a combination of 13 palliative care terms were analysed. The tweet volume for all terms increased by 62.3% between 2011-2012 (262.5K) and 2012-2013 (421K). The most popular terms include 'end-of-life' (210K), #hpm (114K) and 'palliative care' (93.8K). Sentiment was high with 89% of tweets rated more positive than all other tweets sent on Twitter during this period. The term 'Liverpool Care Pathway' experienced the highest percentage increase in tweets (55% increase) reaching a peak in July 2013. A lot of discussion about palliative care is taking place on Twitter, and the majority of this is positive. Social media presents a novel opportunity for engagement and ongoing dialogue with public and professional groups. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The Asymmetric Effects of Investor Sentiment

DEFF Research Database (Denmark)

Lutz, Chandler

2016-01-01

We use the returns on lottery-like stocks and a dynamic factor model to construct a novel index of investor sentiment. This new measure is highly correlated with other behavioral indicators, but more closely tracks speculative episodes. Our main new finding is that the effects of sentiment...... are asymmetric: During peak-to-trough periods of investor sentiment (sentiment contractions), high sentiment predicts low future returns for the cross section of speculative stocks and for the market overall, whereas the relationship between sentiment and future returns is positive but relatively weak during...... trough-to-peak episodes (sentiment expansions). Overall, these results match theories and anecdotal accounts of investor sentiment....

Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

KAUST Repository

Salas-Zárate, María Pilar

2017-03-14

Recent research activities in the areas of opinion mining, sentiment analysis and emotion detection from natural language texts are gaining ground under the umbrella of affective computing. Nowadays, there is a huge amount of text data available in the Social Media (e.g. forums, blogs, and social networks) concerning to users’ opinions about experiences buying products and hiring services. Sentiment analysis or opinion mining is the field of study that analyses people’s opinions and mood from written text available on the Web. In this paper, we present extensive experiments to evaluate the effectiveness of the psychological and linguistic features for sentiment classification. To this purpose, we have used four psycholinguistic dimensions obtained from LIWC, and one stylometric dimension obtained from WordSmith, for the subsequent training of the SVM, Naïve Bayes, and J48 algorithms. Also, we create a corpus of tourist reviews from the travel website TripAdvisor. The findings reveal that the stylometric dimension is quite feasible for sentiment classification. Finally, with regard to the classifiers, SVM provides better results than Naïve Bayes and J48 with an F-measure rate of 90.8%.

Twitter and the health reforms in the English National Health Service.

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King, Dominic; Ramirez-Cano, Daniel; Greaves, Felix; Vlaev, Ivo; Beales, Steve; Darzi, Ara

2013-05-01

Social media (for example Facebook and YouTube) uses online and mobile technologies to allow individuals to participate in, comment on and create user-generated content. Twitter is a widely used social media platform that lets users post short publicly available text-based messages called tweets that other users can respond to. Alongside traditional media outlets, Twitter has been a focus for discussions about the controversial and radical reforms to the National Health Service (NHS) in England that were recently passed into law by the current coalition Government. Looking at over 120,000 tweets made about the health reforms, we have investigated whether any insights can be obtained about the role of Twitter in informing, debating and influencing opinion in a specific area of health policy. In particular we have looked at how the sentiment of tweets changed with the passage of the Health and Social Care Bill through Parliament, and how this compared to conventional opinion polls taken over the same time period. We examine which users appeared to have the most influence in the 'Twittersphere' and suggest how a widely used metric of academic impact - the H-index - could be applied to measure context-dependent influence on Twitter. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Pro-Anorexia and Anti-Pro-Anorexia Videos on YouTube: Sentiment Analysis of User Responses.

Science.gov (United States)

Oksanen, Atte; Garcia, David; Sirola, Anu; Näsi, Matti; Kaakinen, Markus; Keipi, Teo; Räsänen, Pekka

2015-11-12

Pro-anorexia communities exist online and encourage harmful weight loss and weight control practices, often through emotional content that enforces social ties within these communities. User-generated responses to videos that directly oppose pro-anorexia communities have not yet been researched in depth. The aim was to study emotional reactions to pro-anorexia and anti-pro-anorexia online content on YouTube using sentiment analysis. Using the 50 most popular YouTube pro-anorexia and anti-pro-anorexia user channels as a starting point, we gathered data on users, their videos, and their commentators. A total of 395 anorexia videos and 12,161 comments were analyzed using positive and negative sentiments and ratings submitted by the viewers of the videos. The emotional information was automatically extracted with an automatic sentiment detection tool whose reliability was tested with human coders. Ordinary least squares regression models were used to estimate the strength of sentiments. The models controlled for the number of video views and comments, number of months the video had been on YouTube, duration of the video, uploader's activity as a video commentator, and uploader's physical location by country. The 395 videos had more than 6 million views and comments by almost 8000 users. Anti-pro-anorexia video comments expressed more positive sentiments on a scale of 1 to 5 (adjusted prediction [AP] 2.15, 95% CI 2.11-2.19) than did those of pro-anorexia videos (AP 2.02, 95% CI 1.98-2.06). Anti-pro-anorexia videos also received more likes (AP 181.02, 95% CI 155.19-206.85) than pro-anorexia videos (AP 31.22, 95% CI 31.22-37.81). Negative sentiments and video dislikes were equally distributed in responses to both pro-anorexia and anti-pro-anorexia videos. Despite pro-anorexia content being widespread on YouTube, videos promoting help for anorexia and opposing the pro-anorexia community were more popular, gaining more positive feedback and comments than pro-anorexia videos

Mining Twitter to Assess the Public Perception of the "Internet of Things".

Directory of Open Access Journals (Sweden)

Jiang Bian

Full Text Available Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT. We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public's attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

Mining Twitter to Assess the Public Perception of the "Internet of Things".

Science.gov (United States)

Bian, Jiang; Yoshigoe, Kenji; Hicks, Amanda; Yuan, Jiawei; He, Zhe; Xie, Mengjun; Guo, Yi; Prosperi, Mattia; Salloum, Ramzi; Modave, François

2016-01-01

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public's attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

The Online Dissemination of Nature-Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to "Nature-Deficit Disorder".

Science.gov (United States)

Palomino, Marco; Taylor, Tim; Göker, Ayse; Isaacs, John; Warber, Sara

2016-01-19

Evidence continues to grow supporting the idea that restorative environments, green exercise, and nature-based activities positively impact human health. Nature-deficit disorder, a journalistic term proposed to describe the ill effects of people's alienation from nature, is not yet formally recognized as a medical diagnosis. However, over the past decade, the phrase has been enthusiastically taken up by some segments of the lay public. Social media, such as Twitter, with its opportunities to gather "big data" related to public opinions, offers a medium for exploring the discourse and dissemination around nature-deficit disorder and other nature-health concepts. In this paper, we report our experience of collecting more than 175,000 tweets, applying sentiment analysis to measure positive, neutral or negative feelings, and preliminarily mapping the impact on dissemination. Sentiment analysis is currently used to investigate the repercussions of events in social networks, scrutinize opinions about products and services, and understand various aspects of the communication in Web-based communities. Based on a comparison of nature-deficit-disorder "hashtags" and more generic nature hashtags, we make recommendations for the better dissemination of public health messages through changes to the framing of messages. We show the potential of Twitter to aid in better understanding the impact of the natural environment on human health and wellbeing.

Sapiness–sentiment analyser

Directory of Open Access Journals (Sweden)

Jánosi-Rancz Katalin Tünde

2015-12-01

Full Text Available In our ever-evolving world, the importance of social networks is bigger now than ever. The purpose of this paper is to develop a sentiment analyzer for the Hungarian language, which we can then use to analyze any text and conduct further experiments. One such experiment is an application which can interface with social networks, and run sentiment analysis on the logged-in users friends’ posts and comments, while the other experiment is the use of sentiment analysis in order to visualize the evolution of relationships between characters in a text.

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

Directory of Open Access Journals (Sweden)

Muhammad Bilal

2016-07-01

Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

Science.gov (United States)

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

2015-08-25

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

Experimental Research in Operation Management in Engine Room by using Language Sentiment/Opinion Analysis

Directory of Open Access Journals (Sweden)

Dimitris Papachristos

2014-12-01

Full Text Available The paper argues for the necessity of a combination MMR methods (questionnaire, interview and sentiment/opinion techniques to personal satisfaction analysis at the maritime and training education and proposes a generic, but practical research approach for this purpose. The proposed approach concerns the personal satisfaction evaluation of Engine Room simulator systems and combines the speech recording (sentiment/opinion analysis for measuring emotional user responses with usability testing (SUS tool. The experimental procedure presented here is a primary effort to research the emotion analysis (satisfaction of the users-students in Engine Room Simulators. Finally, the ultimate goal of this research is to find and test the critical factors that influence the educational practice and user’s satisfaction of Engine Room Simulator Systems and the ability to conduct full-time system control by the marine crew.

Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter.

Science.gov (United States)

Delir Haghighi, Pari; Kang, Yong-Bin; Buchbinder, Rachelle; Burstein, Frada; Whittle, Samuel

2017-01-19

Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that "pain" and "chronicpain" were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time

Mining Twitter to Assess the Public Perception of the “Internet of Things”

Science.gov (United States)

Yoshigoe, Kenji; Hicks, Amanda; Yuan, Jiawei; He, Zhe; Xie, Mengjun; Guo, Yi; Prosperi, Mattia; Salloum, Ramzi; Modave, François

2016-01-01

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public. PMID:27391760

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

Science.gov (United States)

Cole-Lewis, Heather; Pugatch, Jillian; Sanders, Amy; Varghese, Arun; Posada, Susana; Yun, Christopher; Schwarz, Mary; Augustson, Erik

2015-10-27

Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. The analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday

GIF Video Sentiment Detection Using Semantic Sequence

Directory of Open Access Journals (Sweden)

Dazhen Lin

2017-01-01

Full Text Available With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs.

Anger in Academic Twitter: Sharing, Caring, and Getting Mad Online

Directory of Open Access Journals (Sweden)

Karen Gregory

2018-01-01

Full Text Available This article examines two different cases or “events” in Twitter to understand the role that negative emotions play in online discussions of academic labor. As academic labor conditions deteriorate and academics take to online spaces, they do so to critique, connect, and organize. We suggest that negative emotions may play a productive role in raising awareness of labor issues, as well as serving as a site for organizing across academic hierarchies and beyond the university. Additionally, negative emotions may fuel the production of new networks, personal, and professional connections. However, as we show, anger online can also provoke substantive repercussions, both personally and institutionally. We suggest that paying attention to the role that negative emotions play on Twitter can help academics gain a better sense of how to use their digital labor for collective action.

Building a house of sentiment on sand: Epistemological issues with contempt.

Science.gov (United States)

Lench, Heather C; Bench, Shane W; Perez, Kenneth A

2017-01-01

Contempt shares its features with other emotions, indicating that there is no justification for creating "sentiment" as a new category of feelings. Scientific categories must be created or updated on the basis of evidence. Building a new category on the currently limited contempt literature would be akin to building a house on sand - likely to fall at any moment.

Citation Sentiment Analysis in Clinical Trial Papers.

Science.gov (United States)

Xu, Jun; Zhang, Yaoyun; Wu, Yonghui; Wang, Jingqi; Dong, Xiao; Xu, Hua

2015-01-01

In scientific writing, positive credits and negative criticisms can often be seen in the text mentioning the cited papers, providing useful information about whether a study can be reproduced or not. In this study, we focus on citation sentiment analysis, which aims to determine the sentiment polarity that the citation context carries towards the cited paper. A citation sentiment corpus was annotated first on clinical trial papers. The effectiveness of n-gram and sentiment lexicon features, and problem-specified structure features for citation sentiment analysis were then examined using the annotated corpus. The combined features from the word n-grams, the sentiment lexicons and the structure information achieved the highest Micro F-score of 0.860 and Macro-F score of 0.719, indicating that it is feasible to use machine learning methods for citation sentiment analysis in biomedical publications. A comprehensive comparison between citation sentiment analysis of clinical trial papers and other general domains were conducted, which additionally highlights the unique challenges within this domain.

Sentimentality and Nostalgia in Elderly People in Bulgaria and Greece - Cross-Validity of the Questionnaire SNEP and Cross-Cultural Comparison.

Science.gov (United States)

Stoyanova, Stanislava Yordanova; Giannouli, Vaitsa; Gergov, Teodor Krasimirov

2017-03-01

Sentimentality and nostalgia are two similar psychological constructs, which play an important role in the emotional lives of elderly people who are usually focused on the past. There are two objectives of this study - making cross-cultural comparison of sentimentality and nostalgia among Bulgarian and Greek elderly people using a questionnaire, and establishing the psychometric properties of this questionnaire among Greek elderly people. Sentimentality and nostalgia in elderly people in Bulgaria and Greece were studied by means of Sentimentality and Nostalgia in Elderly People questionnaire (SNEP), created by Gergov and Stoyanova (2013). For the Greek version, one factor structure without sub-scales is proposed, while for the Bulgarian version of SNEP the factor structure had four sub-scales, besides the total score. Together with some similarities (medium level of nostalgia and sentimentality being widespread), the elderly people in Bulgaria and Greece differed cross-culturally in their sentimentality and nostalgia related to the past in direction of more increased sentimentality and nostalgia in the Bulgarian sample. Some gender and age differences revealed that the oldest male Bulgarians were the most sentimental. The psychometric properties of this questionnaire were examined for the first time in a Greek sample of elders and a trend was found for stability of sentimentality and nostalgia in elderly people that could be studied further in longitudinal studies.

The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”

Science.gov (United States)

Palomino, Marco; Taylor, Tim; Göker, Ayse; Isaacs, John; Warber, Sara

2016-01-01

Evidence continues to grow supporting the idea that restorative environments, green exercise, and nature-based activities positively impact human health. Nature-deficit disorder, a journalistic term proposed to describe the ill effects of people’s alienation from nature, is not yet formally recognized as a medical diagnosis. However, over the past decade, the phrase has been enthusiastically taken up by some segments of the lay public. Social media, such as Twitter, with its opportunities to gather “big data” related to public opinions, offers a medium for exploring the discourse and dissemination around nature-deficit disorder and other nature–health concepts. In this paper, we report our experience of collecting more than 175,000 tweets, applying sentiment analysis to measure positive, neutral or negative feelings, and preliminarily mapping the impact on dissemination. Sentiment analysis is currently used to investigate the repercussions of events in social networks, scrutinize opinions about products and services, and understand various aspects of the communication in Web-based communities. Based on a comparison of nature-deficit-disorder “hashtags” and more generic nature hashtags, we make recommendations for the better dissemination of public health messages through changes to the framing of messages. We show the potential of Twitter to aid in better understanding the impact of the natural environment on human health and wellbeing. PMID:26797628

The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”

Directory of Open Access Journals (Sweden)

Marco Palomino

2016-01-01

Full Text Available Evidence continues to grow supporting the idea that restorative environments, green exercise, and nature-based activities positively impact human health. Nature-deficit disorder, a journalistic term proposed to describe the ill effects of people’s alienation from nature, is not yet formally recognized as a medical diagnosis. However, over the past decade, the phrase has been enthusiastically taken up by some segments of the lay public. Social media, such as Twitter, with its opportunities to gather “big data” related to public opinions, offers a medium for exploring the discourse and dissemination around nature-deficit disorder and other nature–health concepts. In this paper, we report our experience of collecting more than 175,000 tweets, applying sentiment analysis to measure positive, neutral or negative feelings, and preliminarily mapping the impact on dissemination. Sentiment analysis is currently used to investigate the repercussions of events in social networks, scrutinize opinions about products and services, and understand various aspects of the communication in Web-based communities. Based on a comparison of nature-deficit-disorder “hashtags” and more generic nature hashtags, we make recommendations for the better dissemination of public health messages through changes to the framing of messages. We show the potential of Twitter to aid in better understanding the impact of the natural environment on human health and wellbeing.

Lexical Sentiment Analysis in Slovenian Texts

OpenAIRE

VOLČANŠEK, MATEJA

2015-01-01

The goal of this thesis is to create a sentiment dictionary for the Slovenian language which can be used in lexical methods for automatic sentiment analysis. We start from a sentiment dictionary for the English language, translate it semi-automatically to Slovenian and curate its content. We test the performance of using the translated dictionary for automated lexical sentiment analysis on a corpus of 5000 manually annotated Slovenian news articles gathered from the main Slovenian news por...

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Science.gov (United States)

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

2015-01-01

Background Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions Social media outlets

Short text sentiment classification based on feature extension and ensemble classifier

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Liu, Yang; Zhu, Xie

2018-05-01

With the rapid development of Internet social media, excavating the emotional tendencies of the short text information from the Internet, the acquisition of useful information has attracted the attention of researchers. At present, the commonly used can be attributed to the rule-based classification and statistical machine learning classification methods. Although micro-blog sentiment analysis has made good progress, there still exist some shortcomings such as not highly accurate enough and strong dependence from sentiment classification effect. Aiming at the characteristics of Chinese short texts, such as less information, sparse features, and diverse expressions, this paper considers expanding the original text by mining related semantic information from the reviews, forwarding and other related information. First, this paper uses Word2vec to compute word similarity to extend the feature words. And then uses an ensemble classifier composed of SVM, KNN and HMM to analyze the emotion of the short text of micro-blog. The experimental results show that the proposed method can make good use of the comment forwarding information to extend the original features. Compared with the traditional method, the accuracy, recall and F1 value obtained by this method have been improved.

Revolutionary Sentiment in Slave Narratives

DEFF Research Database (Denmark)

Simonsen, Karen-Margrethe

2016-01-01

of genre and are written not by the victims but by ‘spectators’, political or human rights agents, historians or literary writers who are in a distanced position from the actual slavery. In order to understand the role of sentimentalism in slave narratives, I will discuss the sentimental novel by Gertrudis...... Gómez de Avellaneda: Sab (the Cuban/Spanish equivalent of Uncle Tom’s Cabin), written between 1836 and 1839 and published 1841 in Spain. I will argue that the sentimentalism of Sab is of a revolutionary kind, based in a conception of natural law and that its aim is a radical transformation of social...... hierarchies. The paper will be divided into three parts: 1. First I will discuss what sentimentalism is and its relation to politics and especially abolitionism in the 19th century. 2. Then I will analyze Sab’s genre trying to establish the different functions of sentimentality, discussing the tragic modes...

Characterizing the followers and tweets of a marijuana-focused Twitter handle.

Science.gov (United States)

Cavazos-Rehg, Patricia; Krauss, Melissa; Grucza, Richard; Bierut, Laura

2014-06-27

Twitter is a popular social media forum for sharing personal experiences, interests, and opinions. An improved understanding of the discourse on Twitter that encourages marijuana use can be helpful for tailoring and targeting online and offline prevention messages. The intent of the study was to assess the content of "tweets" and the demographics of followers of a popular pro-marijuana Twitter handle (@stillblazingtho). We assessed the sentiment and content of tweets (sent from May 1 to December 31, 2013), as well as the demographics of consumers that follow a popular pro-marijuana Twitter handle (approximately 1,000,000 followers) using Twitter analytics from Demographics Pro. This analytics company estimates demographic characteristics based on Twitter behavior/usage, relying on multiple data signals from networks, consumption, and language and requires confidence of 95% or above to make an estimate of a single demographic characteristic. A total of 2590 tweets were sent from @stillblazingtho during the 8-month period and 305 (11.78%) replies to another Twitter user were excluded for qualitative analysis. Of the remaining 2285 tweets, 1875 (82.06%) were positive about marijuana, 403 (17.64%) were neutral, and 7 (0.31%) appeared negative about marijuana. Approximately 1101 (58.72%) of the positive marijuana tweets were perceived as jokes or humorous, 340 (18.13%) implied that marijuana helps you to feel good or relax, 294 (15.68%) mentioned routine, frequent, or heavy use, 193 (10.29%) mentioned blunts, marijuana edibles, or paraphernalia (eg, bongs, vaporizers), and 186 (9.92%) mentioned other risky health behaviors (eg, tobacco, alcohol, other drugs, sex). The majority (699,103/959,143; 72.89%) of @stillblazingtho followers were 19 years old or younger. Among people ages 17 to 19 years, @stillblazingtho was in the top 10% of all Twitter handles followed. More followers of @stillblazingtho in the United States were African American (323,107/759,407; 42.55%) or

Characterizing the Followers and Tweets of a Marijuana-Focused Twitter Handle

Science.gov (United States)

Krauss, Melissa; Grucza, Richard; Bierut, Laura

2014-01-01

Background Twitter is a popular social media forum for sharing personal experiences, interests, and opinions. An improved understanding of the discourse on Twitter that encourages marijuana use can be helpful for tailoring and targeting online and offline prevention messages. Objectives The intent of the study was to assess the content of “tweets” and the demographics of followers of a popular pro-marijuana Twitter handle (@stillblazingtho). Methods We assessed the sentiment and content of tweets (sent from May 1 to December 31, 2013), as well as the demographics of consumers that follow a popular pro-marijuana Twitter handle (approximately 1,000,000 followers) using Twitter analytics from Demographics Pro. This analytics company estimates demographic characteristics based on Twitter behavior/usage, relying on multiple data signals from networks, consumption, and language and requires confidence of 95% or above to make an estimate of a single demographic characteristic. Results A total of 2590 tweets were sent from @stillblazingtho during the 8-month period and 305 (11.78%) replies to another Twitter user were excluded for qualitative analysis. Of the remaining 2285 tweets, 1875 (82.06%) were positive about marijuana, 403 (17.64%) were neutral, and 7 (0.31%) appeared negative about marijuana. Approximately 1101 (58.72%) of the positive marijuana tweets were perceived as jokes or humorous, 340 (18.13%) implied that marijuana helps you to feel good or relax, 294 (15.68%) mentioned routine, frequent, or heavy use, 193 (10.29%) mentioned blunts, marijuana edibles, or paraphernalia (eg, bongs, vaporizers), and 186 (9.92%) mentioned other risky health behaviors (eg, tobacco, alcohol, other drugs, sex). The majority (699,103/959,143; 72.89%) of @stillblazingtho followers were 19 years old or younger. Among people ages 17 to 19 years, @stillblazingtho was in the top 10% of all Twitter handles followed. More followers of @stillblazingtho in the United States were

Money and sentiment: a psychodynamic approach to behavioral finance.

Science.gov (United States)

Mohacsy, Ildiko; Lefer, Heidi

2007-01-01

This article tackles one of the timeliest issues for both practitioners and patients today: sentiment, psychodynamics, and the stock market. Economic bubbles and crashes have occurred regularly through history -- from Holland's 17th century tulip mania, to America's 19th century railway mania, to the 1990s high-tech obsession. Though most investors regard themselves as investing rationally, few do. Instead they react collectively, buying high and selling low in crowds. Being subject to the illusion of control, they follow regressive behavior patterns and irrational, wishful thinking. They are victimized by their own emotions of hope, fear, and uncertainty. Crises happen often in economics. Indeed, the market itself may be quantified as a conglomeration of human sentiment. The relationship between magical thinking and the pictorial language of the market will be explored. Psychodynamic conceptualizations about risk and speculation are discussed, as are the interplay of affects versus judgment, rational thinking, and the knowledge of one's own capacity for stress tolerance.

Sentimentality and Nostalgia in Elderly People in Bulgaria and Greece – Cross-Validity of the Questionnaire SNEP and Cross-Cultural Comparison

Science.gov (United States)

Stoyanova, Stanislava Yordanova; Giannouli, Vaitsa; Gergov, Teodor Krasimirov

2017-01-01

Sentimentality and nostalgia are two similar psychological constructs, which play an important role in the emotional lives of elderly people who are usually focused on the past. There are two objectives of this study - making cross-cultural comparison of sentimentality and nostalgia among Bulgarian and Greek elderly people using a questionnaire, and establishing the psychometric properties of this questionnaire among Greek elderly people. Sentimentality and nostalgia in elderly people in Bulgaria and Greece were studied by means of Sentimentality and Nostalgia in Elderly People questionnaire (SNEP), created by Gergov and Stoyanova (2013). For the Greek version, one factor structure without sub-scales is proposed, while for the Bulgarian version of SNEP the factor structure had four sub-scales, besides the total score. Together with some similarities (medium level of nostalgia and sentimentality being widespread), the elderly people in Bulgaria and Greece differed cross-culturally in their sentimentality and nostalgia related to the past in direction of more increased sentimentality and nostalgia in the Bulgarian sample. Some gender and age differences revealed that the oldest male Bulgarians were the most sentimental. The psychometric properties of this questionnaire were examined for the first time in a Greek sample of elders and a trend was found for stability of sentimentality and nostalgia in elderly people that could be studied further in longitudinal studies. PMID:28344678

Artificial Neural Network methods applied to sentiment analysis

OpenAIRE

Ebert, Sebastian

2017-01-01

Sentiment Analysis (SA) is the study of opinions and emotions that are conveyed by text. This field of study has commercial applications for example in market research (e.g., “What do customers like and dislike about a product?”) and consumer behavior (e.g., “Which book will a customer buy next when he wrote a positive review about book X?”). A private person can benefit from SA by automatic movie or restaurant recommendations, or from applications on the computer or smart phone that adapt to...

On Three Defenses of Sentimentalism

OpenAIRE

Iwasa, Noriaki

2013-01-01

This essay shows that a moral sense or moral sentiments alone cannot identify appropriate morals. To this end, the essay analyzes three defenses of Francis Hutcheson’s, David Hume’s, and Adam Smith’s moral sense theories against the relativism charge that a moral sense or moral sentiments vary across people, societies, cultures, or times. The first defense is the claim that there is a universal moral sense or universal moral sentiments. However, even if they exist, a moral sense or moral sent...

Discourse over a contested technology on Twitter: A case study of hydraulic fracturing.

Science.gov (United States)

Hopke, Jill E; Simis, Molly

2015-10-04

High-volume hydraulic fracturing, a drilling simulation technique commonly referred to as "fracking," is a contested technology. In this article, we explore discourse over hydraulic fracturing and the shale industry on the social media platform Twitter during a period of heightened public contention regarding the application of the technology. We study the relative prominence of negative messaging about shale development in relation to pro-shale messaging on Twitter across five hashtags (#fracking, #globalfrackdown, #natgas, #shale, and #shalegas). We analyze the top actors tweeting using the #fracking hashtag and receiving @mentions with the hashtag. Results show statistically significant differences in the sentiment about hydraulic fracturing and shale development across the five hashtags. In addition, results show that the discourse on the main contested hashtag #fracking is dominated by activists, both individual activists and organizations. The highest proportion of tweeters, those posting messages using the hashtag #fracking, were individual activists, while the highest proportion of @mention references went to activist organizations. © The Author(s) 2015.

The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

Directory of Open Access Journals (Sweden)

Ahmed Imran KABIR

2018-01-01

Full Text Available Apparently, word clouds have grown as a clear and appealing illustration or visualization strategy in terms of text. Word clouds are used as a part of various settings as a way to give a diagram by cleansing text throughout those words that come up with most frequently. Generally, this is performed constantly as an unadulterated text outline. In any case, that there is a bigger capability to this basic yet intense visualization worldview in text analytics. In this work, we investigate the adequacy of word clouds for general text analysis errands and also analyze the tweets to find out the sentiment and also discuss the legal aspects of text mining. We used R software to pull twitter data which depends altogether on word cloud as a visualization technique and also with the help of positive and negative words to determine the user sentiment. We indicate how this approach can be viably used to explain text analysis tasks and assess it in a qualitative user research.

Qualitative and Quantitative Sentiment Proxies

DEFF Research Database (Denmark)

Zhao, Zeyan; Ahmad, Khurshid

2015-01-01

Sentiment analysis is a content-analytic investigative framework for researchers, traders and the general public involved in financial markets. This analysis is based on carefully sourced and elaborately constructed proxies for market sentiment and has emerged as a basis for analysing movements...

Análisis sentimental de la polaridad de tweets en tiempo real aplicando algoritmos de clasificación

OpenAIRE

Campillos García, Eduardo

2017-01-01

El objetivo de este proyecto es el análisis, desarrollo y evaluación de un sistema de clasificación de polaridad sobre un producto determinado a través del contenido generado en Twitter en español. En primer lugar, se ha estudiado la situación actual en el campo del análisis sentimental (AS) y los avances más notables en esta área perteneciente al procesamiento del lenguaje natural. En segundo lugar, una vez estudiado el estado del arte, se pasará a desarrollar el código que implementarem...

Sentiment classification technology based on Markov logic networks

Science.gov (United States)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts.

Science.gov (United States)

Roccetti, Marco; Marfia, Gustavo; Salomoni, Paola; Prandi, Catia; Zagari, Rocco Maurizio; Gningaye Kengni, Faustine Linda; Bazzoli, Franco; Montagnani, Marco

2017-08-09

Data concerning patients originates from a variety of sources on social media. The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients' perspectives on a given medical prescription. To shed light on patients' behavior and concerns, we focused on Crohn's disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn's disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen's kappa coefficient method. The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen's kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was

Naturalizing sentimentalism for environmental ethics

DEFF Research Database (Denmark)

Kasperbauer, Tyler Joshua

2015-01-01

. One way of testing the empirical adequacy of sentimentalism is by looking at research on environmental values. A classic problem in environmental ethics is providing an account of the intrinsic value of nonhuman entities, which is often thought to be inconsistent with sentimentalism. However...

"When 'Bad' is 'Good'": Identifying Personal Communication and Sentiment in Drug-Related Tweets.

Science.gov (United States)

Daniulaityte, Raminta; Chen, Lu; Lamy, Francois R; Carlson, Robert G; Thirunarayan, Krishnaprasad; Sheth, Amit

2016-10-24

To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was

Negative emotions boost user activity at BBC forum

Science.gov (United States)

Chmiel, Anna; Sobkowicz, Pawel; Sienkiewicz, Julian; Paltoglou, Georgios; Buckley, Kevan; Thelwall, Mike; Hołyst, Janusz A.

2011-08-01

We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users were investigated. Scale-free distributions were observed for activity in individual discussion threads as well as for overall activity. The number of unique users in a thread normalized by the thread length decays with thread length, suggesting that thread life is sustained by mutual discussions rather than by independent comments. Automatic sentiment analysis shows that most posts contain negative emotions and the most active users in individual threads express predominantly negative sentiments. It follows that the average emotion of longer threads is more negative and that threads can be sustained by negative comments. An agent-based computer simulation model has been used to reproduce several essential characteristics of the analyzed system. The model stresses the role of discussions between users, especially emotionally laden quarrels between supporters of opposite opinions, and represents many observed statistics of the forum.

Examining Public Perceptions about Lead in School Drinking Water: A Mixed-Methods Analysis of Twitter Response to an Environmental Health Hazard.

Science.gov (United States)

Ekenga, Christine C; McElwain, Cora-Ann; Sprague, Nadav

2018-01-20

Exposure to lead has long been a community health concern in St. Louis, Missouri. The objective of this study was to examine public response to reports of elevated lead levels in school drinking water in St. Louis, Missouri via Twitter, a microblogging platform with over 320 million active users. We used a mixed-methods design to examine Twitter user status updates, known as "tweets," from 18 August to 31 December 2016. The number of tweets each day was recorded, and Twitter users were classified into five user types (General Public, Journalist/News, Health Professional/Academic, Politician/Government Official, and Non-Governmental Organization). A total of 492 tweets were identified during the study period. The majority of discourse on Twitter occurred during the two-week period after initial media reports and was driven by members of the General Public. Thematic analysis of tweets revealed four themes: Information Sharing, Health Concerns, Sociodemographic Disparities, and Outrage. Twitter users characterized lead in school drinking water as an issue of environmental inequity. The findings of this study provide evidence that social media platforms can be utilized as valuable tools for public health researchers and practitioners to gauge public sentiment about environmental health issues, identify emerging community concerns, and inform future communication and research strategies regarding environmental health hazards.

Monitoring the environment and human sentiment on the Great Barrier Reef: Assessing the potential of collective sensing.

Science.gov (United States)

Becken, Susanne; Stantic, Bela; Chen, Jinyan; Alaei, Ali Reza; Connolly, Rod M

2017-12-01

With the growth of smartphone usage the number of social media posts has significantly increased and represents potentially valuable information for management, including of natural resources and the environment. Already, evidence of using 'human sensor' in crises management suggests that collective knowledge could be used to complement traditional monitoring. This research uses Twitter data posted from the Great Barrier Reef region, Australia, to assess whether the extent and type of data could be used to Great Barrier Reef organisations as part of their monitoring program. The analysis reveals that large amounts of tweets, covering the geographic area of interest, are available and that the pool of information providers is greatly enhanced by the large number of tourists to this region. A keyword and sentiment analysis demonstrates the usefulness of the Twitter data, but also highlights that the actual number of Reef-related tweets is comparatively small and lacks specificity. Suggestions for further steps towards the development of an integrative data platform that incorporates social media are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

The gene patent controversy on Twitter: a case study of Twitter users' responses to the CHEO lawsuit against Long QT gene patents.

Science.gov (United States)

Du, Li; Kamenova, Kalina; Caulfield, Timothy

2015-08-25

The recent Canadian lawsuit on patent infringement, filed by the Children's Hospital of Eastern Ontario (CHEO), has engendered a significant public debate on whether patenting genes should be legal in Canada. In part, this public debate has involved the use of social networking sites, such as Twitter. This case provides an opportunity to examine how Twitter was used in the context of this gene patent controversy. We collected 310 English-language tweets that contained the keyword "gene patents" by using TOPSY.com and Twitter's built-in search engine. A content analysis of the messages was conducted to establish the users' perspectives on both CHEO's court challenge and the broader controversy over the patenting of human DNA. More specifically, we analyzed the users' demographics, geographic locations, and attitudes toward the CHEO position on gene patents and the patentability of human genes in principle. Our analysis has shown that messages tweeted by news media and health care organizations were re-tweeted most frequently in Twitter discussions regarding both the CHEO patent infringement lawsuit and gene patents in general. 34.8% of tweets were supportive of CHEO, with 52.8% of the supportive tweets suggesting that gene patents contravene patients' rights to health care access. 17.6% of the supportive tweets cited ethical and social concerns against gene patents. Nearly 40% of tweets clearly expressed that human genes should not be patentable, and there were no tweets that presented perspectives favourable toward the patenting of human genes. Access to healthcare and the use of genetic testing were the most important concerns raised by Twitter users in the context of the CHEO case. Our analysis of tweets reveals an expectation that the CHEO lawsuit will provide an opportunity to clear the confusion on gene patents by establishing a legal precedent on the patentability of human genes in Canada. In general, there were no tweets arguing in favour of gene patents

Weibo sentiments and stock return: A time-frequency view

Science.gov (United States)

Liu, Zhixin; Zhao, Jichang; Su, Chiwei

2017-01-01

This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter’s variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which have both frequency and time-varying features. Five detailed sentiments are positively related to the stock return for certain periods, particularly since October 2014 at medium to high frequencies of less than ten trading days, when the stock return is undergoing significant fluctuations. Sadness appears to have a closer relationship with the stock return than the other four sentiments. As to the lead-lag relationships, the stock return causes Weibo sentiments rather than reverse for most of the periods with significant linkages. Compared with polarity sentiments (negative vs. positive), detailed sentiments provide more information regarding relationships between Weibo sentiments and the stock market. The stock market exerts positive effects on bullishness and agreement of microblogs. Meanwhile, agreement leads the stock return in-phase at the frequency of approximately 40 trading days, indicating that less disagreement improves certainty about the stock market. PMID:28672026

A Peculiar Sentiment Analysis Advancement in Big Data

Science.gov (United States)

Valera, Manisha; Patel, Yash

2018-01-01

Every company wants to discover what its customers feel about it. But sentiment analysis can get coarser and turn inward to improve employee as well as customers’ satisfaction. A term called sentiment analysis, or the mathematical taxonomy of statements’ negative or positive connotations, gives companies potent ways to analyse cumulative language data across all sorts of communications. There’s real value in measuring sentiment inside and outside your company. Our big data experts analyse the core grounds of the problems raised while handling giant datasets and find solutions to efficiently manage massive datasets with ease. We built a sentiment analysis solution to measure positive and negative sentiments with top rating products for Amazon.com‘s Electronics products as against competitor products.

A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words.

Science.gov (United States)

Wang, Bingkun; Huang, Yongfeng; Wu, Xian; Li, Xing

2015-01-01

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

Directory of Open Access Journals (Sweden)

Bernd Resch

2016-07-01

Full Text Available Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens’ ideas and feedback in participatory sensing approaches like “People as Sensors”. As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens’ emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics, based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none. Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab’s performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens’ perceptions of the city.

A New Index of Housing Sentiment

DEFF Research Database (Denmark)

Bork, Lasse; Møller, Stig Vinther; Pedersen, Thomas Quistgaard

We propose a new measure for housing sentiment and show that it accurately tracks expectations about future house price growth rates. We construct the housing sentiment index using partial least squares on questions related to consumers' opinions of buying conditions for houses from University...

A New Index of Housing Sentiment

DEFF Research Database (Denmark)

Bork, Lasse; Møller, Stig Vinther; Pedersen, Thomas Quistgaard

We propose a new measure for housing sentiment and show that it accurately tracks expectations about future house price growth rates. We construct the housing sentiment index using partial least squares on household survey responses to questions about buying conditions for houses. We …find...

Sentiment Polarization and Balance among Users in Online Social Networks

DEFF Research Database (Denmark)

Hillmann, Robert; Trier, Matthias

2012-01-01

Communication within online social network applications enables users to express and share sentiments electronically. Existing studies examined the existence or distribution of sentiments in online communication at a general level or in small-observed groups. Our paper extends this research...... by analyzing sentiment exchange within social networks from an ego-network perspective. We draw from research on social influence and social attachment to develop theories of node polarization, balance effects and sentiment mirroring within communication dyads. Our empirical analysis covers a multitude...... of social networks in which the sentiment valence of all messages was determined. Subsequently we studied ego-networks of focal actors (ego) and their immediate contacts. Results support our theories and indicate that actors develop polarized sentiments towards individual peers but keep sentiment in balance...

A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words

Directory of Open Access Journals (Sweden)

Bingkun Wang

2015-01-01

Full Text Available With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words

Science.gov (United States)

Huang, Yongfeng; Wu, Xian; Li, Xing

2015-01-01

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods. PMID:26106409

Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

Directory of Open Access Journals (Sweden)

Fawaz H.H. Mahyoub

2014-12-01

Full Text Available Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.

Topic Modeling in Sentiment Analysis: A Systematic Review

Directory of Open Access Journals (Sweden)

Toqir Ahmad Rana

2016-06-01

Full Text Available With the expansion and acceptance of Word Wide Web, sentiment analysis has become progressively popular research area in information retrieval and web data analysis. Due to the huge amount of user-generated contents over blogs, forums, social media, etc., sentiment analysis has attracted researchers both in academia and industry, since it deals with the extraction of opinions and sentiments. In this paper, we have presented a review of topic modeling, especially LDA-based techniques, in sentiment analysis. We have presented a detailed analysis of diverse approaches and techniques, and compared the accuracy of different systems among them. The results of different approaches have been summarized, analyzed and presented in a sophisticated fashion. This is the really effort to explore different topic modeling techniques in the capacity of sentiment analysis and imparting a comprehensive comparison among them.

Perceptions of Secondhand E-Cigarette Aerosol Among Twitter Users.

Science.gov (United States)

Unger, Jennifer B; Escobedo, Patricia; Allem, Jon-Patrick; Soto, Daniel W; Chu, Kar-Hai; Cruz, Tess

2016-04-01

There is considerable debate among the public health community about the health risks of secondhand exposure to the aerosol from electronic cigarettes (e-cigarettes). Despite mounting scientific evidence on the chemical content of e-cigarette aerosol, public perceptions of the relative safety of secondhand e-cigarette aerosol have not been well characterized. This study collected tweets, or messages sent using Twitter, about exposure to secondhand e-cigarette aerosol over a 6-week period in 2015. Tweets were coded on sentiment about e-cigarettes (pro-, anti-, or neutral/unknown) and topic (health, social, advertisement, or unknown). The 1519 tweets included 531 pro-e-cigarette tweets, 392 anti-e-cigarette tweets, and 596 neutral tweets. Social tweets far outnumbered health tweets (747 vs. 182, respectively). Social-focused tweets were predominantly pro-e-cigarette, whereas health-focused tweets were predominantly anti-e-cigarette. Twitter discussions about secondhand vaping are dominated by pro-e-cigarette social tweets, although there is a presence of anti-e-cigarette social tweets and tweets about negative and positive health effects. Public health and regulatory agencies could use social media and traditional media to disseminate the message that e-cigarette aerosol contains potentially harmful chemicals and could be perceived as offensive. This study identifies the prevalent topics and opinions that could be incorporated into health education messages.

Telkom UData sentiment analysis using crowdsourcing and trust

Science.gov (United States)

Noer, Edvya; Sulistyo Kusumo, Dana; Rusmawati, Yanti

2018-03-01

Microblogging sites have millions of people sharing their thoughts daily because of its characteristic short and simple manner of expression. Sentiments analysis are often being used to analyse the user customer opinions regarding brand images or products. For some reasons, not all sentiment generated using this existing machine-based algorithms yields satisfying results. This is mostly due to the uniformity of the informal language used in the social media sentences. This condition also occurs in Telkom UData on our preliminary study, where the machine-based provided less then optimal results in analysing the sentiment. This research offers concepts with human interaction using crowdsourcing where people are involved to analyse sentiments, while forming the new training dataset at the same time. From the research results found that sarcastic and contradictory sentences can be recognized by humans, to be utilized as new training datasets for further machine learning. From this experiments, that approach are likely increase the accuracy of the sentiments in UData from neutral to become positive or negative polarized up to 39%. We do as well simulated trust concept through sociometric to ensure the crowdsource workers are trusted and capable enough in analysing the sentiments on social media.

A mathematical model of sentimental dynamics accounting for marital dissolution.

Science.gov (United States)

Rey, José-Manuel

2010-03-31

Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law.

A mathematical model of sentimental dynamics accounting for marital dissolution.

Directory of Open Access Journals (Sweden)

José-Manuel Rey

Full Text Available BACKGROUND: Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. METHODOLOGY/PRINCIPAL FINDINGS: Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. CONCLUSIONS/SIGNIFICANCE: These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law.

Machine learning in sentiment reconstruction of the simulated stock market

Science.gov (United States)

Goykhman, Mikhail; Teimouri, Ali

2018-02-01

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Wealth dynamics in a sentiment-driven market

Science.gov (United States)

Goykhman, Mikhail

2017-12-01

We study dynamics of a simulated world with stock and money, driven by the externally given processes which we refer to as sentiments. The considered sentiments influence the buy/sell stock trading attitude, the perceived price uncertainty, and the trading intensity of all or a part of the market participants. We study how the wealth of market participants evolves in time in such an environment. We discuss the opposite perspective in which the parameters of the sentiment processes can be inferred a posteriori from the observed market behavior.

A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

OpenAIRE

Wang, Yibo; Wang, Mingming; Xu, Wei

2018-01-01

Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the propo...

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

Science.gov (United States)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

EFL Teachers' Commitment to Professional Ethics and Their Emotional Intelligence: A Relationship Study

Science.gov (United States)

Ashraf, Hamid; Hosseinnia, Mansooreh; Domsky, Javad GH.

2017-01-01

Emotional intelligence is the capability to realize, to create, to comprehend emotions and sentimental knowledge, and to reflectively control emotions and to improve emotional and mental growth. The purpose of this study is to examine the relationship between EFL teachers' commitment to professional ethics and their emotional intelligence. To…

ARCHITECTURE OF A SENTIMENT ANALYSIS PLATFORM

Directory of Open Access Journals (Sweden)

CRISTIAN BUCUR

2015-06-01

Full Text Available A new domain of research evolved in the last decade, called sentiment analysis that tries to extract knowledge from opinionated text documents. The article presents an overview of the domain and present an architecture of a system that could perform sentiment analysis processes. Based on previous researches are presented two methods for performing classification and the results obtained.

A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network

Directory of Open Access Journals (Sweden)

Meizi Li

2015-01-01

Full Text Available User sentiment analysis has become a flourishing frontier in data mining mobile social network platform since the mobile social network plays a significant role in users’ daily communication and sentiment interaction. This study studies the scheme of sentiment estimate by using the users’ trustworthy relationships for evaluating sentiment delivering. First, we address an overview of sentiment delivering estimate scheme and propose its related definitions, that is, trust chain among users, sentiment semantics, and sentiment ontology. Second, this study proposes the trust chain model and its evaluation method, which is composed of evaluation of atomic, serial, parallel, and combined trust chains. Then, we propose sentiment modeling method by presenting its modeling rules. Further, we propose the sentiment delivering estimate scheme from two aspects: explicit and implicit sentiment delivering estimate schemes, based on trust chain and sentiment modeling method. Finally, examinations and results are given to further explain effectiveness and feasibility of our scheme.

Fine-grained Emotion Role Detection Based on Retweet Information

OpenAIRE

Yu, Zhiwen; Chen, Liming; Guo, Bin; Ma, Chao; Yi, Fei; Wang, Zhu

2018-01-01

User behaviors in online social networks convey not only literal information but also one’s emotion attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting ...

Understanding Social Media’s Take on Climate Change through Large-Scale Analysis of Targeted Opinions and Emotions

Energy Technology Data Exchange (ETDEWEB)

Pathak, Neetu; Henry, Michael J.; Volkova, Svitlana

2017-03-29

Social media is a powerful data source for researchers interested in understanding population-level behavior, having been successfully leveraged in a number of different application areas including flu and illness prediction models, detecting civil unrest, and measuring public sentiment towards a given topic of interest within the public discourse. In this work, we present a study of a large collection of Twitter data centered on the social conversation around global cli- mate change during the UN Climate Change Conference, held in Paris, France during December 2015 (COP21). We first developed a mechanism for distinguishing between personal and non-personal accounts. We then analyzed demographics and emotion and opinion dynamics over time and location in order to understand how the different user types converse around meaningful topics on social media. This methodology offers an in-depth insight into the behavior and opinions around a topic where multiple distinct narratives are present, and lays the groundwork for future work in studying narratives in social media.

Sentiment analysis enhancement with target variable in Kumar’s Algorithm

Science.gov (United States)

Arman, A. A.; Kawi, A. B.; Hurriyati, R.

2016-04-01

Sentiment analysis (also known as opinion mining) refers to the use of text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews discussion that is being talked in social media for many purposes, ranging from marketing, customer service, or public opinion of public policy. One of the popular algorithm for Sentiment Analysis implementation is Kumar algorithm that developed by Kumar and Sebastian. Kumar algorithm can identify the sentiment score of the statement, sentence or tweet, but cannot determine the relationship of the object or target related to the sentiment being analysed. This research proposed solution for that challenge by adding additional component that represent object or target to the existing algorithm (Kumar algorithm). The result of this research is a modified algorithm that can give sentiment score based on a given object or target.

Sentiment analysis to determine the impact of online messages on smokers' choices to use varenicline.

Science.gov (United States)

Cobb, Nathan K; Mays, Darren; Graham, Amanda L

2013-12-01

Social networks are a prominent component of online smoking cessation interventions. This study applied sentiment analysis-a data processing technique that codes textual data for emotional polarity-to examine how exposure to messages about the cessation drug varenicline affects smokers' decision making around its use. Data were from QuitNet, an online social network dedicated to smoking cessation and relapse prevention. Self-reported medication choice at registration and at 30 days was coded among new QuitNet registrants who participated in at least one forum discussion mentioning varenicline between January 31, 2005 and March 9, 2008. Commercially available software was used to code the sentiment of forum messages mentioning varenicline that occurred during this time frame. Logistic regression analyses examined whether forum message exposure predicted medication choice. The sample of 2132 registrants comprised mostly women (78.3%), white participants (83.4%), averaged 41.2 years of age (SD = 10.9), and smoked on average 21.5 (SD = 9.7) cigarettes/day. After adjusting for potential confounders, as exposure to positive varenicline messages outweighed negative messages, the odds of switching to varenicline (odds ratio = 2.05, 95% confidence interval = 1.66 to 2.54) and continuing to use varenicline (odds ratio = 2.46, 95% confidence interval = 1.96 to 3.10) statistically significantly increased. Sentiment analysis is a useful tool for analyzing text-based data to examine their impact on behavior change. Greater exposure to positive sentiment in online conversations about varenicline is associated with a greater likelihood that smokers will choose to use varenicline in a quit attempt.

Emotional reactions to human reproductive cloning.

Science.gov (United States)

May, Joshua

2016-01-01

Extant surveys of people's attitudes towards human reproductive cloning focus on moral judgements alone, not emotional reactions or sentiments. This is especially important given that some (especially Leon Kass) have argued against such cloning on the ground that it engenders widespread negative emotions, like disgust, that provide a moral guide. To provide some data on emotional reactions to human cloning, with a focus on repugnance, given its prominence in the literature. This brief mixed-method study measures the self-reported attitudes and emotions (positive or negative) towards cloning from a sample of participants in the USA. Most participants condemned cloning as immoral and said it should be illegal. The most commonly reported positive sentiment was by far interest/curiosity. Negative emotions were much more varied, but anxiety was the most common. Only about a third of participants selected disgust or repugnance as something they felt, and an even smaller portion had this emotion come to mind prior to seeing a list of options. Participants felt primarily interested and anxious about human reproductive cloning. They did not primarily feel disgust or repugnance. This provides initial empirical evidence that such a reaction is not appropriately widespread. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Ce “sentiment de culpabilité” That “Guilty Feeling” : Emotions and Motivation in Migration and Transnational Caregiving’

Directory of Open Access Journals (Sweden)

Loretta Baldassar

2010-10-01

Full Text Available Cet article explore l’expérience de la “culpabilité” en tant qu’émotion jouant un rôle moteur dans les obligations réciproques de soin dans les relations familiales transnationales. Je pose l’hypothèse qu’en créant une séparation physique, une absence et un sentiment de manque, l’acte migratoire pousse les migrants à se sentir coupables de ne pas remplir l’obligation morale de coprésence. Les migrants se sentent souvent coupables de ne pas être présents pour s’occuper de leurs parents âgés ; les parents se sentent souvent coupables de ne pas participer à la vie de leurs enfants et de leurs petits-enfants. Ce “sentiment de culpabilité” les encourage à “rester en contact” aussi souvent et effectivement que possible en créant des occasions leur permettant d’échanger coprésence et don de soi. En renforçant les relations, en exerçant une influence sur autrui, et en allégeant les inégalités relationnelles, la culpabilité peut servir au final à renforcer et maintenir les relations de soin transnationales. Cependant, dans les relations transnationales où les devoirs sont trop lourds pour être remplis, les individus peuvent se désengager du soin afin d’éviter des sentiments de culpabilité accablants et fragilisants.This paper explores the experience of “guilt” as a motivating emotion in reciprocal obligations to care in transnational kinship relations. My hypothesis is that the act of migration, by causing physical separation, absence and longing, causes migrants to feel guilty about their moral obligations to be co-present. For instance, migrants often feel guilty about not being present to look after ageing parents; similarly, parents often feel guilty about not participating in the lives of their children and especially grandchildren. This “guilty feeling” motivates them to “stay in touch” as often and as effectively as they can by creating opportunities in which they can

Influence and Dissemination Of Sentiments in Social Network Communication Patterns

DEFF Research Database (Denmark)

Hillmann, Robert; Trier, Matthias

2013-01-01

Previous research suggests the existence of sentiments in online social networks. In comparison to real life human interaction, in which sentiments have been shown to have an influence on human behaviour, it is not yet completely understood which mechanisms explain how sentiments influence users ...... that express the same sentiment polarization. We interpret these findings and suggest future research to advance our currently limited theories that assume perceived and generalized social influence to path-dependent social influence models that consider actual behaviour....

Twitter classification model: the ABC of two million fitness tweets.

Science.gov (United States)

Vickey, Theodore A; Ginis, Kathleen Martin; Dabrowski, Maciej

2013-09-01

The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.

Vaccine Images on Twitter: Analysis of What Images are Shared

Science.gov (United States)

Dredze, Mark

2018-01-01

Background Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. Objective The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. Methods We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Results Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet’s textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. Conclusions We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. PMID:29615386

Vaccine Images on Twitter: Analysis of What Images are Shared.

Science.gov (United States)

Chen, Tao; Dredze, Mark

2018-04-03

Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet's textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. ©Tao Chen, Mark Dredze. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.04.2018.

Quantifying Sentiment and Influence in Blogspaces

Energy Technology Data Exchange (ETDEWEB)

Hui, Peter SY; Gregory, Michelle L.

2010-07-25

The weblog, or blog, has become a popular form of social media, through which authors can write posts, which can in turn generate feedback in the form of user comments. When considered in totality, a collection of blogs can thus be viewed as a sort of informal collection of mass sentiment and opinion. An obvious topic of interest might be to mine this collection to obtain some gauge of public sentiment over the wide variety of topics contained therein. However, the sheer size of the so-called blogosphere, combined with the fact that the subjects of posts can vary over a practically limitless number of topics poses some serious challenges when any meaningful analysis is attempted. Namely, the fact that largely anyone with access to the Internet can author their own blog, raises the serious issue of credibility— should some blogs be considered to be more influential than others, and consequently, when gauging sentiment with respect to a topic, should some blogs be weighted more heavily than others? In addition, as new posts and comments can be made on almost a constant basis, any blog analysis algorithm must be able to handle such updates efficiently. In this paper, we give a formalization of the blog model. We give formal methods of quantifying sentiment and influence with respect to a hierarchy of topics, with the specific aim of facilitating the computation of a per-topic, influence-weighted sentiment measure. Finally, as efficiency is a specific endgoal, we give upper bounds on the time required to update these values with new posts, showing that our analysis and algorithms are scalable.

Lexicon-based sentient analysis by mapping conveyed sentiment to intended sentiment

NARCIS (Netherlands)

Hogenboom, A.; Bal, M.; Frasincar, F.; Bal, D.; Kaymak, U.; De Jong, F.

2014-01-01

As consumers nowadays generate increasingly more content describing their experiences with, e.g., products and brands in various languages, information systems monitoring a universal, language-independent measure of peoples intended sentiment are crucial for todays businesses. In order to facilitate

Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis

OpenAIRE

Guillem Aguado; Vicente Julian; Ana Garcia-Fornes

2018-01-01

The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a user having an affective state considered negative by an Analyzer can affect other users and generate bad repercussions. Those Analyzers that we propose are a Sentiment Analyzer, a Stress Analyzer and a novel combined Analyzer. We also want to discov...

Twitter Analytics: Are the U.S. Coastal Regions Prepared for Climate Change in 2017?

Science.gov (United States)

Singleton, S. L.; Kumar, S.

2017-12-01

According to the U.S. National Climate Assessment, the Southeast Coast and Gulf Coast of the United States are particularly susceptible to sea level rise, heat waves, hurricanes and less accessibility to clean water due to climate change. This is because of the extreme variation of topography in these two regions. Preparation for climate change consequences can only occur with conversation, which is a method of bringing awareness to the issue. Over the past decade, social media has taken over the spectrum of information exchange in the United States. Social Network Analysis (SNA) is a field that is emerging with the growth in popularity of social media. SNA is the practice of analyzing trends in volume and opinion of a population of social media users. Twitter, one popular social media platform, is one of the largest microblogging sites in the world, and it provides an abundance of data related to the trending topics such as climate change. Twitter analytics is a type of SNA performed on data from the tweets of Twitter users. In this work, Twitter analytics is performed on the data generated from the Twitter users in the United States, who were talking about climate change, global warming and/or CO2, over the course of one year (July 2016 - June 2017). Specifically, a regional comparative analysis on the coastal U.S. regions was conducted to recognize which region(s) is/are falling behind on the conversation about climate change. Sentiment analysis was also performed to understand the trends in opinion about climate change that vary over time. Experimental results determined that the southeast coast of the United States is deficient in their discussion about climate change compared to the other coastal regions. Igniting the conversation about this issue in these regions will mitigate the disasters due to climate change by increasing awareness in the people of these regions so they can properly prepare.

Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter.

Science.gov (United States)

van Lent, Liza Gg; Sungur, Hande; Kunneman, Florian A; van de Velde, Bob; Das, Enny

2017-06-13

In 2014, the world was startled by a sudden outbreak of Ebola. Although Ebola infections and deaths occurred almost exclusively in Guinea, Sierra Leone, and Liberia, few potential Western cases, in particular, caused a great stir among the public in Western countries. This study builds on the construal level theory to examine the relationship between psychological distance to an epidemic and public attention and sentiment expressed on Twitter. Whereas previous research has shown the potential of social media to assess real-time public opinion and sentiment, generalizable insights that further the theory development lack. Epidemiological data (number of Ebola infections and fatalities) and media data (tweet volume and key events reported in the media) were collected for the 2014 Ebola outbreak, and Twitter content from the Netherlands was coded for (1) expressions of fear for self or fear for others and (2) psychological distance of the outbreak to the tweet source. Longitudinal relations were compared using vector error correction model (VECM) methodology. Analyses based on 4500 tweets revealed that increases in public attention to Ebola co-occurred with severe world events related to the epidemic, but not all severe events evoked fear. As hypothesized, Web-based public attention and expressions of fear responded mainly to the psychological distance of the epidemic. A chi-square test showed a significant positive relation between proximity and fear: χ 2 2 =103.2 (Pfear for self in the Netherlands showed peaks when Ebola became spatially closer by crossing the Mediterranean Sea and Atlantic Ocean. Fear for others was mostly predicted by the social distance to the affected parties. Spatial and social distance are important predictors of public attention to worldwide crisis such as epidemics. These factors need to be taken into account when communicating about human tragedies. ©Liza GG van Lent, Hande Sungur, Florian A Kunneman, Bob van de Velde, Enny Das

Sentiments and Perceptions of Business Respondents on Social Media: an Exploratory Analysis

Directory of Open Access Journals (Sweden)

Torres van Grinsven Vanessa

2015-06-01

Full Text Available The perceptions and sentiments of business respondents are considered important for statistical bureaus. As perceptions and sentiments are related to the behavior of the people expressing them, gaining insights into the perceptions and sentiments of business respondents is of interest to understand business survey response. In this article we present an exploratory analysis of expressions in the social media regarding Statistics Netherlands. In recent years, social media have become an important infrastructure for communication flows and thus an essential network in our social structure. Within that network participants are actively involved in expressing sentiments and perceptions. The results of our analysis provide insights into the perceptions and sentiments that business respondents have of this national statistical institute and specifically its business surveys. They point towards the specific causes that have led to a positive or a negative sentiment. Based on these results, recommendations aimed at influencing the perceptions and sentiments will be discussed, with the ultimate goal of stimulating survey participation. We also suggest recommendations regarding social media studies on sentiments and perceptions of survey respondents.

Determining negation scope and strength in sentiment analysis

NARCIS (Netherlands)

Hogenboom, A.C.; Iterson, van P.; Heerschop, B.M.W.T.; Frasincar, F.; Kaymak, U.

2011-01-01

A key element for decision makers to track is their stakeholders' sentiment. Recent developments show a tendency of including various aspects other than word frequencies in automated sentiment analysis approaches. One of these aspects is negation, which can be accounted for in various ways. We

Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network.

Directory of Open Access Journals (Sweden)

Xiaoqing Hao

Full Text Available To study the sentiment diffusion of online public opinions about hot events, we collected people's posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P, weakly positive (p, neutral (o, weakly negative (n, and strongly negative (N. These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts' sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people's sentiments regarding online hot events according to the sentiment diffusion mechanism.

Sentiment Analysis of User-Generated Content on Drug Review Websites

Directory of Open Access Journals (Sweden)

Na, Jin-Cheon

2015-03-01

Full Text Available This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names to semantic types in the Unified Medical Language System (UMLS Semantic Network.

The Relationship between Sentiment and Risk in Financial Markets

Directory of Open Access Journals (Sweden)

Ana Luiza Paraboni

2018-03-01

Full Text Available This article estimates association coefficients between measures of market sentiment and risk in the U.S., German and Chinese markets. In terms of risk, four measures were considered: standard deviation, value at risk, expected shortfall and shortfall deviation risk. For market sentiment, data was collected using the Psych Signal technology, which is based on the behavior of investors on social networks. The results indicate significant statistical associations, with the direction of association having financial meaning. Moreover, the empirical findings are valid for all risk measurements. The results are in keeping with the Prospect Theory, since in moments when the sentiment indicates low liquidity (a negative value for the difference between Bullish and Bearish Intensities investors try to reduce the negotiation volume, which has a positive impact on risk. On the other hand, under the inverted scenario, when sentiment indicates high liquidity, there is an increase in the negotiation volume and a consequent decrease in risk. This article is important because its observations of market sentiment as measured by social media data show a consistent relationship with measures of financial risk.

Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Directory of Open Access Journals (Sweden)

Łukasz Augustyniak

2015-12-01

Full Text Available We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature.

A Video Recall Study of In-session Changes in Sentiment Override.

Science.gov (United States)

Johnson, Lee N; Tambling, Rachel B; Anderson, Shayne R

2015-09-01

This study examines in-session changes in sentiment override over the first three sessions of couple therapy. Couples viewed a video recording of therapy sessions immediately after each of the first three sessions and continuously rated their level of sentiment override. Ninety-eight changes were randomly chosen for analysis. Three talk turns prior to each change was coded using the Family Relational Communication Control Coding System. Results show that changes in sentiment override occur frequently. Repeated incidents of communication control were related to negative change in sentiment override for females. Repeated incidents of being left out of the conversation were related to negative changes in sentiment override for females and positive changes for males. © 2014 Family Process Institute.

EFFECT OF INVESTOR SENTIMENT ON FUTURE RETURNS IN THE NIGERIAN STOCK MARKET

Directory of Open Access Journals (Sweden)

Udoka Bernard Alajekwu

2017-06-01

Full Text Available The study examined the effect of investor sentiment on future returns in the Nigerian stock market. The OLS regression and granger causality techniques were employed for data analyses. The results showed that (1 investor sentiment has a significant positive effect on stock market returns even after control for fundamentals such as Industrial production index, consumer price index and Treasury bill rate; (2 there is a uni-directional causality that runs from change in investor sentiment (ΔCCI to stock market returns (Rm. Derived finding showed that the inclusion of fundamentals increased the explanatory power of investor sentiment from 3.96% to 33.05%, though at both level, investor sentiment (ΔCCI has low explanatory power on stock market returns. The study posits existence of a dynamic relationship between investor sentiment and the behaviour of stock future returns in Nigeria such that higher sentiment concurrently leads to higher stock prices.

Use of Twitter Polls to Determine Public Opinion Regarding Content Presented at a Major National Specialty Society Meeting.

Science.gov (United States)

Rosenkrantz, Andrew B; Hawkins, C Matthew

2017-02-01

The aim of this study was to evaluate the feasibility of using Twitter polls to assess public opinion regarding session content at a national specialty society meeting. Twitter polls allow users to embed multiple-choice questions within tweets and automatically aggregate responses. Two radiologists attending the 2016 annual meeting of the ACR posted a Twitter poll containing the hashtag #ACR2016 during 10 meeting sessions addressing socioeconomics/advocacy, patient experience, and social media/informatics (20 polls total). Each poll contained a question asking for an opinion regarding the session's content. Polls were open for responses for 24 hours. The average number of responses per poll was significantly higher for the user with the larger number of Twitter followers (24.3 ± 14.4 versus 11.2 ± 9.8, P = .015). A total of 57% of respondents agreed that radiologists' payments should shift to value-based payments, and 86% agreed that radiologists should routinely survey their patients to monitor quality; however, 83% disagreed with basing physician payments on patient satisfaction scores. A total of 85% disagreed that the artificial intelligence supercomputer Watson will entirely replace radiologists. A total of 76% agreed that social media can drive business at less cost than standard marketing. A total of 56% agreed with the direction of the ACR's advocacy and regulatory efforts, whereas 74% considered the ACR's advocacy efforts to be moderately or very useful for their practice. A total of 50% planned to change their practice on the basis of keynote remarks by Dr Ezekiel Emanuel. Twitter polls provide a free and easy infrastructure to potentially capture global public sentiment during the course of a medical society meeting. Their use may enrich and promote discussions of key session content. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Health organizations providing and seeking social support: a Twitter-based content analysis.

Science.gov (United States)

Rui, Jian Raymond; Chen, Yixin; Damiano, Amanda

2013-09-01

Providing and seeking social support are important aspects of social exchange. New communication technologies, especially social network sites (SNSs), facilitate the process of support exchange. An increasing number of health organizations are using SNSs. However, how they provide and seek social support via SNSs has yet to garner academic attention. This study examined the types of social support provided and sought by health organizations on Twitter. A content analysis was conducted on 1,500 tweets sent by a random sample of 58 health organizations within 2 months. Findings indicate that providing informational and emotional support, as well as seeking instrumental support, were the main types of social support exchanged by health organizations through Twitter. This study provides a typology for studying social support exchanges by health organizations, and recommends strategies for health organizations regarding the effective use of Twitter.

The Twitter Book

CERN Document Server

O'Reilly, Tim

2009-01-01

This practical guide will teach you everything you need to know to quickly become a Twitter power user, including strategies and tactics for using Twitter's 140-character messages as a serious--and effective--way to boost your business. Co-written by Tim O'Reilly and Sarah Milstein, widely followed and highly respected Twitterers, the practical information in The Twitter Book is presented in a fun, full-color format that's packed with helpful examples and clear explanations.A Sneak Preview on SlideShare

Using Social Media Sentiment Analysis for Interaction Design Choices

DEFF Research Database (Denmark)

McGuire, Mark; Kampf, Constance Elizabeth

2015-01-01

Social media analytics is an emerging skill for organizations. Currently, developers are exploring ways to create tools for simplifying social media analysis. These tools tend to focus on gathering data, and using systems to make it meaningful. However, we contend that making social media data...... meaningful is by nature a human-computer interaction problem. We examine this problem around the emerging field of sentiment analysis, exploring criteria for designing sentiment analysis systems based in Human Computer interaction, HCI. We contend that effective sentiment analysis affects audience analysis...

Rule-based emotion detection on social media : putting tweets on Plutchik's wheel

NARCIS (Netherlands)

Tromp, E.; Pechenizkiy, M.

2014-01-01

We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different

Social Media Sentiment Analysis and Topic Detection for Singapore English

Science.gov (United States)

2013-09-01

study of NLP techniques,” La Revista de Procesamiento de Lenguaje Natural , vol. 50, pp. 45–52, 2013. [5] F. Batista, and R. Ribeiro, “Sentiment...have been made possible via social-media applications. Sentiment analysis and topic detection are two growing areas in Natural Language Processing...social-media applications. Sentiment analysis and topic detection are two growing areas in Natural Language Processing, and there are increasing

Twitter=quitter? An analysis of Twitter quit smoking social networks.

Science.gov (United States)

Prochaska, Judith J; Pechmann, Cornelia; Kim, Romina; Leonhardt, James M

2012-07-01

Widely popular, Twitter, a free social networking and micro-blogging service, offers potential for health promotion. This study examined the activity of Twitter quit smoking social network accounts. A cross-sectional analysis identified 153 activated Twitter quit smoking accounts dating back to 2007 and examined recent account activity for the month of August 2010. The accounts had a median of 155 followers and 82 total tweets per account; 49% of accounts had >100 tweets. Posted content was largely inconsistent with clinical guidelines; 48% linked to commercial sites for quitting smoking and 43% had tweets on e-cigarettes. In August 2010, 81 of the accounts (53%) were still active. Though popular for building quit smoking social networks, many of the Twitter accounts were no longer active, and tweet content was largely inconsistent with clinical guidelines. Future research is needed to examine the effectiveness of Twitter for supporting smoking cessation.

网民对“人祸”事件的道德情绪特点--基于微博大数据研究%The characteristics of moral emotions of Chinese netizens towards an anthropogenic hazard：A sentiment analysis on Weibo

Institute of Scientific and Technical Information of China (English)

叶勇豪; 许燕; 朱一杰; 梁炯潜; 兰天; 于淼

2016-01-01

本研究采用大数据研究方法,对爬取的“动车事故”发生后40天内的94,562条相关微博进行情感分析,以探讨网民对“人祸”的道德情绪特点,同时对不同群体情绪表达差异进行探讨。结果发现：(1)网民对于动车事故主要表达的道德情绪有：愤怒、鄙视、厌恶、同情和爱。(2)包含不同道德基础的事件与不同的道德情绪相关联；(3)对于愤怒、厌恶和鄙视,男性普遍有更高的表达倾向和表达强度,而女性更倾向于表达爱和同情且强度更高；(4)对于爱和同情,团体VIP用户组表达的可能性和强度都高于其他用户；个体VIP用户比非VIP用户更可能表达愤怒、鄙视和厌恶,而团体VIP用户表达这类情绪的强度最小。研究表明,虚拟网络中人们道德情绪特点依然符合道德基础理论；不同群体在表达道德情绪时的差异性是对道德基础理论相关研究的补充。总言之,数据挖掘技术和情感分析方法是进行情绪研究的有效手段。%Weibo provides its users a cyber platform to share opinions and show their emotions towards issues at home and abroad. In the process, massive amounts of data are made, and becomes the raw material for sentiment analysis. Previous studies in related fields of computer science and communication focused mainly on developing better sentiment analysis techniques to analyze basic emotions. To add a new perspective, this paper focused on studying the moral emotions expressed toward the “7.23 Wenzhou Train Collision” by Chinese netizens on Weibo. In particular, we analyzed the frequencies of different moral emotions expressed, and related them to the temporal occurrence of different moral events (e.g., statements made by the authority or victims that have moral implications) in the aftermath of the collision, and how different patterns of moral emotions were expressed by different groups including male and female, VIP and non-VIP users

Sentiment and art prices

NARCIS (Netherlands)

Penasse, J.N.G.; Renneboog, L.D.R.; Spaenjers, C.

We hypothesize the existence of a slow-moving fad component in art prices. Using unique panel survey data on art market participants’ confidence levels in the outlook for a set of artists, we find that sentiment indeed predicts short-term returns.

Analyzing online sentiment to predict telephone poll results.

Science.gov (United States)

Fu, King-wa; Chan, Chee-hon

2013-09-01

The telephone survey is a common social science research method for capturing public opinion, for example, an individual's values or attitudes, or the government's approval rating. However, reducing domestic landline usage, increasing nonresponse rate, and suffering from response bias of the interviewee's self-reported data pose methodological challenges to such an approach. Because of the labor cost of administration, a phone survey is often conducted on a biweekly or monthly basis, and therefore a daily reflection of public opinion is usually not available. Recently, online sentiment analysis of user-generated content has been deployed to predict public opinion and human behavior. However, its overall effectiveness remains uncertain. This study seeks to examine the temporal association between online sentiment reflected in social media content and phone survey poll results in Hong Kong. Specifically, it aims to find the extent to which online sentiment can predict phone survey results. Using autoregressive integrated moving average time-series analysis, this study suggested that online sentiment scores can lead phone survey results by about 8-15 days, and their correlation coefficients were about 0.16. The finding is significant to the study of social media in social science research, because it supports the conclusion that daily sentiment observed in social media content can serve as a leading predictor for phone survey results, keeping as much as 2 weeks ahead of the monthly announcement of opinion polls. We also discuss the practical and theoretical implications of this study.

THE EFFECT OF INVESTOR SENTIMENT ON ISE SECTOR INDICES

Directory of Open Access Journals (Sweden)

SERPİL CANBAŞ

2013-06-01

Full Text Available Determining the factors that affect stock returns is one of the most investigated topics of the finance literature. A number of models have been developed to explain stock returns. Some of these models maintain that stock returns are generated rationally. These models are, Capital Asset Pricing Model, Index Models, Arbitrage Pricing Model and Macroeconomic Factor Models. Nevertheless, these models could not have explained stock returns, although they have used different parameters and methods. Some studies have maintained that investor psychology would have a role in the stock return generation process. There are three theories that investigate the effect of investor psychology on financial markets: Mental accounting theory, herd behavior theory and investor sentiment theory. The aim of this study is to investigate the effect of investor sentiment on stock returns. In this context, three investor sentiment proxies have been determined in the light of previous studies. These proxies are closed-end fund discount, average fund flow of mutual funds and the ratio of net stock purchases of foreign investors to ISE market capitalization. ISE sector indices are used to proxy stock returns. On the other hand, there is a possibility that investor sentiment would merely reflect economic innovations. Some economic factors are used as control variables in order to examine this possibility. Regression analyses are employed for investigating the effect of investor sentiment on stock returns. Findings suggest that investor sentiment affect stock returns systematically. This finding keeps its robustness when economic variables are added to the model.

An Empirical Study of the Effect of Investor Sentiment on Returns of Different Industries

Directory of Open Access Journals (Sweden)

Chuangxia Huang

2014-01-01

Full Text Available Studies on investor sentiment are mostly focused on the stock market, but little attention has been paid to the effect of investor sentiment on the return of a specific industry. This paper constructs a proxy variable to examine the relationship between investor sentiment and the return of a specific industry, using the Principle Component Analysis, and finds that investor sentiment is positively correlated with the industry return of the current period and negatively correlated with that of one lag period; we classify investor sentiment as optimistic state and pessimistic state and find that optimistic investor sentiment has a positive effect on stock returns of most industries, while pessimistic investor sentiment has no effect on them; this paper further builds a two-state Markov regime switching model and finds that sentiment has different effect on different industries returns on different states of market.

The Twitter Book

CERN Document Server

O'Reilly, Tim

2011-01-01

Twitter is not just for talking about your breakfast anymore. It's become an indispensable communications tool for businesses, non-profits, celebrities, and people around the globe. With the second edition of this friendly, full-color guide, you'll quickly get up to speed not only on standard features, but also on new options and nuanced uses that will help you tweet with confidence. Co-written by two widely recognized Twitter experts, The Twitter Book is packed with all-new real-world examples, solid advice, and clear explanations guaranteed to turn you into a power user. Use Twitter to con

Twitter Application Development For Dummies

CERN Document Server

Reagan, Dusty

2010-01-01

Get the guide to planning, developing and monetizing apps for Twitter!. Twitter is the one of the hottest trends in social networking. With several million users, Twitter's popularity is growing everyday. Twitter will continue to increase user base while third-party companies keep popping up all over to make money building Twitter apps for phones, advertising tools, analytics/management tools and more. Some of the most popular Twitter apps include TwitPic, Twhirl, TweetDeck, and FriendorFollow. With this book, author Dusty Reagan uses one of his unpublished Twitter app ideas and takes you thro

MedlinePlus.gov on Twitter

Science.gov (United States)

... page please turn Javascript on. MedlinePlus.gov on Twitter Past Issues / Fall 2009 Table of Contents You can now follow MedlinePlus.gov on Twitter: twitter.com/medlineplus4you The medlineplus4you Twitter feed provides ...

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding.

Science.gov (United States)

Haselmayer, Martin; Jenny, Marcelo

2017-01-01

Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Anomalies and Investor Sentiment: Empirical Evidences in the Brazilian Market

Directory of Open Access Journals (Sweden)

Gustavo Correia Xavier

2017-10-01

Full Text Available This study examined the relationship between investor sentiment and value anomalies in Brazil. In addition, it analyzed if pricing deviations caused by investors with optimistic views are different from those caused by pessimistic investors. The sample included all non-financial firms listed on the B3 (Brasil, Bolsa, Balcão stock exchange from July 1999 to June 2014. We used the Principal Component Analysis multivariate technique to capture the component common to four different proxies for investor sentiment. The study empirically tested the index series and its variation on the return series of Long-Short portfolios of 12 anomaly-based strategies. The study found that the measure of the sentiment index had a partial explanatory power for the anomalies only when included in the CAPM. Yet, when using the index sentiment changes as an explanatory variable, the study found a relationship with future returns, robust to all risk factors. Thus, it is possible to relate investor sentiment index to anomaly-based portfolio returns. When analyzing average returns after optimistic and pessimistic periods, the values we found in our empirical test were not statistically significant enough to infer the possible existence of short-sale constraints.

SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks.

Science.gov (United States)

Rodrigues, Ramon Gouveia; das Dores, Rafael Marques; Camilo-Junior, Celso G; Rosa, Thierson Couto

2016-01-01

Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression, for instance. Thus, monitoring the mood of the patients is an important part of their treatment. Many cancer patients are users of online social networks and many of them take part in cancer virtual communities where they exchange messages commenting about their treatment or giving support to other patients in the community. Most of these communities are of public access and thus are useful sources of information about the mood of patients. Based on that, Sentiment Analysis methods can be useful to automatically detect positive or negative mood of cancer patients by analyzing their messages in these online communities. The objective of this work is to present a Sentiment Analysis tool, named SentiHealth-Cancer (SHC-pt), that improves the detection of emotional state of patients in Brazilian online cancer communities, by inspecting their posts written in Portuguese language. The SHC-pt is a sentiment analysis tool which is tailored specifically to detect positive, negative or neutral messages of patients in online communities of cancer patients. We conducted a comparative study of the proposed method with a set of general-purpose sentiment analysis tools adapted to this context. Different collections of posts were obtained from two cancer communities in Facebook. Additionally, the posts were analyzed by sentiment analysis tools that support the Portuguese language (Semantria and SentiStrength) and by the tool SHC-pt, developed based on the method proposed in this paper called SentiHealth. Moreover, as a second alternative to analyze the texts in Portuguese, the collected texts were automatically translated

Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization

NARCIS (Netherlands)

Gao, B.; Berendt, B.; Vanschoren, J.

2016-01-01

Understanding users’ sentiment expression in social media is important in many domains, such as marketing and online applications. Is one demographic group inherently different from another? Does a group express the same sentiment both in private and public? How can we compare the sentiments of

Studying User Income through Language, Behaviour and Affect in Social Media.

Science.gov (United States)

Preoţiuc-Pietro, Daniel; Volkova, Svitlana; Lampos, Vasileios; Bachrach, Yoram; Aletras, Nikolaos

2015-01-01

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Studying User Income through Language, Behaviour and Affect in Social Media.

Directory of Open Access Journals (Sweden)

Daniel Preoţiuc-Pietro

Full Text Available Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Quantifying Salient Concepts Discussed in Social Media Content: A Case Study using Twitter Content Written by Radicalized Youth

Directory of Open Access Journals (Sweden)

Shadi Ghajar-Khosravi

2016-05-01

Full Text Available Social Media has become an important source for information about people and real-world events. Its importance is driven largely by the enormous number of people generating and updating content in Social Media platforms. In this report, we measure the extent to which we can accurately measure the salience of topics/concepts that might be of interest to an analyst, and evaluate whether concepts like positive and negative sentiment can be meaningfully extracted from Social Media content. As a test case, we examined Twitter content generated by female users who are sympathetic to the Islamic State in Iraq and Syria (ISIS. The basic technique proposed here can be developed further to create a more fine-grained exanimation of Social Media content.

Naturalist Sentimentalism

DEFF Research Database (Denmark)

Wohlmann, Anita

2018-01-01

This article examines four short stories by the American writer Rebecca Harding Davis (1831-1910), who became a nationally acclaimed writer with the stylistically innovative novella Life in the Iron Mills (1861). Using the double perspective of age studies and ‘naturalist sentimentalism’, the essay...... analyses Davis’s representation of the paradoxes of old age. Davis blends sentimental ideals of sympathy, sacrifice, and hope with naturalist themes of entrapment, the inevitability of decline, and biological determinism. Four short stories by Davis will serve as cases in point: ‘At Noon’ (1887), ‘At...

Revisiting the investor sentiment-stock returns relationship: A multi-scale perspective using wavelets

Science.gov (United States)

Lao, Jiashun; Nie, He; Jiang, Yonghong

2018-06-01

This paper employs SBW proposed by Baker and Wurgler (2006) to investigate the nonlinear asymmetric Granger causality between investor sentiment and stock returns for US economy while considering different time-scales. The wavelet method is utilized to decompose time series of investor sentiment and stock returns at different time-scales to focus on the local analysis of different time horizons of investors. The linear and nonlinear asymmetric Granger methods are employed to examine the Granger causal relationship on similar time-scales. We find evidence of strong bilateral linear and nonlinear asymmetric Granger causality between longer-term investor sentiment and stock returns. Furthermore, we observe the positive nonlinear causal relationship from stock returns to investor sentiment and the negative nonlinear causal relationship from investor sentiment to stock returns.

The Use of Twitter by Radiology Journals: An Analysis of Twitter Activity and Impact Factor.

Science.gov (United States)

Kelly, Brendan S; Redmond, Ciaran E; Nason, Gregory J; Healy, Gerard M; Horgan, Niall A; Heffernan, Eric J

2016-11-01

Medical journals use social media as a means to disseminate new research and interact with readers. The microblogging site Twitter is one such platform. The aim of this study was to analyze the recent use of Twitter by the leading radiology journals. The top 50 journals by Impact Factor were included. Twitter profiles associated with these journals, or their corresponding societies, were identified. Whether each journal used other social media platforms was also recorded. Each Twitter profile was analyzed over a one-year period, with data collected via Twitonomy software. Klout scores of social media influence were calculated. Results were analyzed in SPSS using Student's t test, Fisher contingency tables, and Pearson correlations to identify any association between social media interaction and Impact Factors of journals. Fourteen journals (28%) had dedicated Twitter profiles. Of the 36 journals without dedicated Twitter profiles, 25 (50%) were associated with societies that had profiles, leaving 11 (22%) journals without a presence on Twitter. The mean Impact Factor of all journals was 3.1 ± 1.41 (range, 1.7-6.9). Journals with Twitter profiles had higher Impact Factors than those without (mean, 3.37 vs 2.14; P Twitter profiles and those associated with affiliated societies (P = .47). Since joining Twitter, 7 of the 11 journals (64%) experienced increases in Impact Factor. A greater number of Twitter followers was correlated with higher journal Impact Factor (R 2  = 0.581, P = .029). The investigators assessed the prevalence and activity of the leading radiology journals on Twitter. Radiology journals with Twitter profiles have higher Impact Factors than those without profiles, and the number of followers of a journal's Twitter profile is positively associated with Impact Factor. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study

Science.gov (United States)

Borondo, J.; Morales, A. J.; Losada, J. C.; Benito, R. M.

2012-06-01

Transmitting messages in the most efficient way as possible has always been one of politicians' main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians' actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore, we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally, we develop a network growth model to reproduce the interactions taking place among politicians.

Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study.

Science.gov (United States)

Borondo, J; Morales, A J; Losada, J C; Benito, R M

2012-06-01

Transmitting messages in the most efficient way as possible has always been one of politicians' main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians' actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore, we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally, we develop a network growth model to reproduce the interactions taking place among politicians.

Wavelets and Sentiment in the Heterogeneous Agents Model

Czech Academy of Sciences Publication Activity Database

Vácha, Lukáš; Vošvrda, Miloslav

2008-01-01

Roč. 15, č. 25 (2008), s. 41-56 ISSN 1212-074X R&D Projects: GA ČR GP402/08/P207; GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990 Institutional research plan: CEZ:AV0Z10750506 Keywords : heterogeneous agents model * market sentiment * Hurst exponent * wavelets Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2008/E/vacha-wavelets and sentiment in the heterogeneous agents model.pdf

Sentiment Analysis of Web Sites Related to Vaginal Mesh Use in Pelvic Reconstructive Surgery.

Science.gov (United States)

Hobson, Deslyn T G; Meriwether, Kate V; Francis, Sean L; Kinman, Casey L; Stewart, J Ryan

2018-05-02

The purpose of this study was to utilize sentiment analysis to describe online opinions toward vaginal mesh. We hypothesized that sentiment in legal Web sites would be more negative than that in medical and reference Web sites. We generated a list of relevant key words related to vaginal mesh and searched Web sites using the Google search engine. Each unique uniform resource locator (URL) was sorted into 1 of 6 categories: "medical", "legal", "news/media", "patient generated", "reference", or "unrelated". Sentiment of relevant Web sites, the primary outcome, was scored on a scale of -1 to +1, and mean sentiment was compared across all categories using 1-way analysis of variance. Tukey test evaluated differences between category pairs. Google searches of 464 unique key words resulted in 11,405 URLs. Sentiment analysis was performed on 8029 relevant URLs (3472 legal, 1625 "medical", 1774 "reference", 666 "news media", 492 "patient generated"). The mean sentiment for all relevant Web sites was +0.01 ± 0.16; analysis of variance revealed significant differences between categories (P Web sites categorized as "legal" and "news/media" had a slightly negative mean sentiment, whereas those categorized as "medical," "reference," and "patient generated" had slightly positive mean sentiments. Tukey test showed differences between all category pairs except the "medical" versus "reference" in comparison with the largest mean difference (-0.13) seen in the "legal" versus "reference" comparison. Web sites related to vaginal mesh have an overall mean neutral sentiment, and Web sites categorized as "medical," "reference," and "patient generated" have significantly higher sentiment scores than related Web sites in "legal" and "news/media" categories.

Mining twitter: a source for psychological wisdom of the crowds.

Science.gov (United States)

Reips, Ulf-Dietrich; Garaizar, Pablo

2011-09-01

Over the last few years, microblogging has gained prominence as a form of personal broadcasting media where information and opinion are mixed together without an established order, usually tightly linked with current reality. Location awareness and promptness provide researchers using the Internet with the opportunity to create "psychological landscapes"--that is, to detect differences and changes in voiced (twittered) emotions, cognitions, and behaviors. In our article, we present iScience Maps, a free Web service for researchers, available from http://maps.iscience.deusto.es/ and http://tweetminer.eu/ . Technologically, the service is based on Twitter's streaming and search application programming interfaces (APIs), accessed through several PHP libraries, and a JavaScript frontend. This service allows researchers to assess via Twitter the effect of specific events in different places as they are happening and to make comparisons between cities, regions, or countries regarding psychological states and their evolution in the course of an event. In a step-by-step example, it is shown how to replicate a study on affective and personality characteristics inferred from first names (Mehrabian & Piercy, Personality and Social Psychology Bulletin, 19, 755-758 1993) by mining Twitter data with iScience Maps.Results from the original study are replicated in both world regions we tested (the western U.S. and the U.K./Ireland); we also discover base rate of names to be a confound that needs to be controlled for in future research.

Measuring e-Commerce service quality from online customer review using sentiment analysis

Science.gov (United States)

Kencana Sari, Puspita; Alamsyah, Andry; Wibowo, Sulistyo

2018-03-01

The biggest e-Commerce challenge to understand their market is to chart their level of service quality according to customer perception. The opportunities to collect user perception through online user review is considered faster methodology than conducting direct sampling methodology. To understand the service quality level, sentiment analysis methodology is used to classify the reviews into positive and negative sentiment for five dimensions of electronic service quality (e-Servqual). As case study in this research, we use Tokopedia, one of the biggest e-Commerce service in Indonesia. We obtain the online review comments about Tokopedia service quality during several month observations. The Naïve Bayes classification methodology is applied for the reason of its high-level accuracy and support large data processing. The result revealed that personalization and reliability dimension required more attention because have high negative sentiment. Meanwhile, trust and web design dimension have high positive sentiments that means it has very good services. The responsiveness dimension have balance sentiment positive and negative.

Sentiments and Perceptions of Business Respondents on Social Media: an Exploratory Analysis

OpenAIRE

Torres van Grinsven Vanessa; Snijkers Ger

2015-01-01

The perceptions and sentiments of business respondents are considered important for statistical bureaus. As perceptions and sentiments are related to the behavior of the people expressing them, gaining insights into the perceptions and sentiments of business respondents is of interest to understand business survey response. In this article we present an exploratory analysis of expressions in the social media regarding Statistics Netherlands. In recent years, social media have become an import...

Twitter and society

CERN Document Server

Weller, Katrin; Burgess, Jean; Mahrt, Merja

2013-01-01

Since its launch in 2006, Twitter has evolved from a niche service to a mass phenomenon; it has become instrumental for everyday communication as well as for political debates, crisis communication, marketing, and cultural participation. But the basic idea behind it has stayed the same: users may post short messages (tweets) of up to 140 characters and follow the updates posted by other users. Drawing on the experience of leading international Twitter researchers from a variety of disciplines and contexts, this is the first book to document the various notions and concepts of Twitter communica

Accentuate the Positive: Positive Sentiments and Status in Task Groups

Science.gov (United States)

Bianchi, Alison J.; Lancianese, Donna A.

2007-01-01

We explore the capacity of positive sentiments, those enduring affective states one achieves when one likes another, to impact status structures. Do positive sentiments combine with existing aspects of interaction to create status hierarchies and potentially change the social order, or do they moderate the effects of extant structure by dampening…

Twitter Tips, Tricks, and Tweets

CERN Document Server

McFedries, Paul

2010-01-01

Maximize your fun and boost your productivity with this updated, full-color guide to tantalizing Twitter tips!. The popularity of Twitter continues to soar, and is fast becoming the most popular social networking site online. Whether you're looking to learn how to set up an account for the first time or are on the prowl for some cool third-party Twitter apps, this full-color guide will boost your entire Twitter experience. Allowing you to communicate with fellow Twitters within a 140-character limit, this fun and fascinating social networking tool is easier than maintaining a blog and quicker

Logical Entity Level Sentiment Analysis

DEFF Research Database (Denmark)

Petersen, Niklas Christoffer; Villadsen, Jørgen

2017-01-01

We present a formal logical approach using a combinatory categorial grammar for entity level sentiment analysis that utilizes machine learning techniques for efficient syntactical tagging and performs a deep structural analysis of the syntactical properties of texts in order to yield precise resu...

The Impact of Conventional and Unconventional Monetary Policy on Investor Sentiment

DEFF Research Database (Denmark)

Lutz, Chandler

2015-01-01

This paper examines the relationship between monetary policy and investor sentiment across conventional and unconventional monetary policy regimes. During conventional times, we find that a surprise decrease in the fed funds rate leads to a large increase in investor sentiment. Similarly, when...... the fed funds rate is at its zero lower bound, research results indicate that expansionary unconventional monetary policy shocks also have a large and positive impact on investor mood. Together, our findings highlight the importance of both conventional and unconventional monetary policy...... in the determination of investor sentiment....

Contrasting Public Opinion Dynamics and Emotional Response during Crisis

Energy Technology Data Exchange (ETDEWEB)

Volkova, Svitlana; Chetviorkin, Ilia; Arendt, Dustin L.; Van Durme, Ben

2016-11-15

We propose an approach for contrasting spatiotemporal dynamics of public opinions expressed toward targeted entities, also known as stance detection task, in Russia and Ukraine during crisis. Our analysis relies on a novel corpus constructed from posts on the VKontakte social network, centered on local public opinion of the ongoing Russian-Ukrainian crisis, along with newly annotated resources for predicting expressions of fine-grained emotions including joy, sadness, disgust, anger, surprise and fear. Akin to prior work on sentiment analysis we align traditional public opinion polls with aggregated automatic predictions of sentiments for contrastive geo-locations. We report interesting observations on emotional response and stance variations across geo-locations. Some of our findings contradict stereotypical misconceptions imposed by media, for example, we found posts from Ukraine that do not support Euromaidan but support Putin, and posts from Russia that are against Putin but in favor USA. Furthermore, we are the first to demonstrate contrastive stance variations over time across geo-locations using storyline visualization technique.

Gratitude as moral sentiment: emotion-guided cooperation in economic exchange.

Science.gov (United States)

DeSteno, David; Bartlett, Monica Y; Baumann, Jolie; Williams, Lisa A; Dickens, Leah

2010-04-01

Economic exchange often pits options for selfish and cooperative benefit against one another. Decisions favoring communal profit at the expense of self-interest have traditionally been thought to stem from strategic control aimed at tamping down emotional responses centered on immediate resource acquisition. In the present article, evidence is provided to argue against this limited view of the role played by emotion in shaping prosociality. Findings demonstrate that the social emotion gratitude functions to engender cooperative economic exchange even at the expense of greater individual financial gains. Using real-time inductions, increased gratitude is shown to directly mediate increased monetary giving within the context of an economic game, even where such giving increases communal profit at the expense of individual gains. Moreover, increased giving occurred regardless of whether the beneficiary was a known individual or complete stranger, thereby removing the possibility that it stemmed from simple awareness of reciprocity constraints. Copyright 2010 APA, all rights reserved.

Social Medicine: Twitter in Healthcare.

Science.gov (United States)

Pershad, Yash; Hangge, Patrick T; Albadawi, Hassan; Oklu, Rahmi

2018-05-28

Social media enables the public sharing of information. With the recent emphasis on transparency and the open sharing of information between doctors and patients, the intersection of social media and healthcare is of particular interest. Twitter is currently the most popular form of social media used for healthcare communication; here, we examine the use of Twitter in medicine and specifically explore in what capacity using Twitter to share information on treatments and research has the potential to improve care. The sharing of information on Twitter can create a communicative and collaborative atmosphere for patients, physicians, and researchers and even improve quality of care. However, risks involved with using Twitter for healthcare discourse include high rates of misinformation, difficulties in verifying the credibility of sources, overwhelmingly high volumes of information available on Twitter, concerns about professionalism, and the opportunity cost of using physician time. Ultimately, the use of Twitter in healthcare can allow patients, healthcare professionals, and researchers to be more informed, but specific guidelines for appropriate use are necessary.

Gauchos que lloran: masculinidades sentimentales en el imaginario criollista / Gauchos who Cry: Sentimental Masculinities in Criollo Imaginary

Directory of Open Access Journals (Sweden)

Ana Peluffo

2013-01-01

Full Text Available The present paper studies a canonical text in Argentinean literature, the Martín Fierro (1872-1878, by José Hernández. The objective is to explore, based on a debate on masculinity and nation,the sentimental side of criollo culture. Although Hernández’s gaucho is usually thought of as the archetype of a stoic masculinity that represses its emotions in order to fight wars at the border, this paper will show how that predominating notion undermines the tearful affective excesses constantly taking place in the text.

Aspect-based sentiment analysis to review products using Naïve Bayes

Science.gov (United States)

Mubarok, Mohamad Syahrul; Adiwijaya, Aldhi, Muhammad Dwi

2017-08-01

Product reviews can provide great benefits for consumers and producers. Number of reviews could be ranging from hundreds to thousands and containing various opinions. These make the process of analyzing and extracting information on existing reviews become increasingly difficult. In this research, sentiment analysis was used to analyze and extract sentiment polarity on product reviews based on a specific aspect of the product. This research was conducted in three phases, such as data preprocessing which involves part-of-speech (POS) tagging, feature selection using Chi Square, and classification of sentiment polarity of aspects using Naïve Bayes. Based on evaluation results, it is known that the system is able to perform aspect-based sentiment analysis with its highest F1-Measure of 78.12%.

Aspect level sentiment analysis using machine learning

Science.gov (United States)

Shubham, D.; Mithil, P.; Shobharani, Meesala; Sumathy, S.

2017-11-01

In modern world the development of web and smartphones increases the usage of online shopping. The overall feedback about product is generated with the help of sentiment analysis using text processing.Opinion mining or sentiment analysis is used to collect and categorized the reviews of product. The proposed system uses aspect leveldetection in which features are extracted from the datasets. The system performs pre-processing operation such as tokenization, part of speech and limitization on the data tofinds meaningful information which is used to detect the polarity level and assigns rating to product. The proposed model focuses on aspects to produces accurate result by avoiding the spam reviews.

Information, Sentiment, and Price in a Fast Order-Driven Market

Czech Academy of Sciences Publication Activity Database

Derviz, Alexis

2011-01-01

Roč. 8, č. 3 (2011), s. 43-75 ISSN 0972-916X Institutional research plan: CEZ:AV0Z10750506 Keywords : limit order * market order * high frequency trading * price dicovery * sentiment Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/derviz-information, sentiment, and price in a fast order-driven market.pdf

Does Online Investor Sentiment Affect the Asset Price Movement? Evidence from the Chinese Stock Market

Directory of Open Access Journals (Sweden)

Chi Xie

2017-01-01

Full Text Available With the quick development of the Internet, online platforms that provide financial news and opinions have attracted more and more attention from investors. The question whether investor sentiment expressed on the Internet platforms has an impact on asset return has not been fully addressed. To this end, this paper uses the Baidu Searching Index as the agent variable to detect the effect of online investor sentiment on the asset price movement in the Chinese stock market. The empirical study shows that although there is a cointegration relationship between online investor sentiment and asset return, the sentiment has a poor ability to predict the price, return, and volatility of asset price. Meanwhile, the structural break points of online investor sentiment do not lead to changes in the asset price movement. Based on the empirical mode decomposition of online investor sentiment, we find that high frequency components of online investor sentiment can be used to predict the asset price movement. Thus, the obtained results could be useful for risk supervision and asset portfolio management.

Twitter data analytics

CERN Document Server

Bruns, Axel; Lewandowski, Dirk

2014-01-01

It might still sound strange to dedicate an entire ebook exclusively to a single Internet platform. But it is not the company Twitter, Inc. that is the focus; this ebook is not about a platform and its features and services. It is about its users and the ways in which they interact with one another via the platform, about the situations that motivate people to share their thoughts publicly, using Twitter as a means to reach out to one another. And it is about the digital traces people leave behind when interacting with Twitter, and most of all about the ways in which these traces - as a new ty

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media.

Directory of Open Access Journals (Sweden)

Hoh Kim

Full Text Available While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in. The majority of users who directly conversed with corporate representatives on the new media were not typical consumers, but experts and practitioners. We extend the existing cognitive model and suggest the audiences' psychological reaction model to describe the information processing process during and after an organizational crisis and response. We also discuss various measures through which companies can respond to a crisis properly in social media in a fashion that is different from conventional mass media.

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media.

Science.gov (United States)

Kim, Hoh; Park, Jaram; Cha, Meeyoung; Jeong, Jaeseung

2015-01-01

While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in. The majority of users who directly conversed with corporate representatives on the new media were not typical consumers, but experts and practitioners. We extend the existing cognitive model and suggest the audiences' psychological reaction model to describe the information processing process during and after an organizational crisis and response. We also discuss various measures through which companies can respond to a crisis properly in social media in a fashion that is different from conventional mass media.

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media

Science.gov (United States)

Cha, Meeyoung; Jeong, Jaeseung

2015-01-01

While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in. The majority of users who directly conversed with corporate representatives on the new media were not typical consumers, but experts and practitioners. We extend the existing cognitive model and suggest the audiences’ psychological reaction model to describe the information processing process during and after an organizational crisis and response. We also discuss various measures through which companies can respond to a crisis properly in social media in a fashion that is different from conventional mass media. PMID:25951231

Twitter-Based EFL Pronunciation Instruction

Science.gov (United States)

Mompean, José Antonio; Fouz-González, Jonás

2016-01-01

This paper looks at the use of "Twitter" as a language teaching/learning tool. It describes the results of a study aimed at testing "Twitter's" effectiveness for pronunciation teaching. The purpose of the study was to determine whether "Twitter" can foster online participation and whether it may have a positive effect…

Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event

Science.gov (United States)

David, Clarissa C.; Ong, Jonathan Corpus; Legara, Erika Fille T.

2016-01-01

When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda) as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response. PMID:27019425

Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event.

Directory of Open Access Journals (Sweden)

Clarissa C David

Full Text Available When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response.

The use of social media in endourology: an analysis of the 2013 World Congress of Endourology meeting.

Science.gov (United States)

Canvasser, Noah E; Ramo, Christina; Morgan, Todd M; Zheng, Kai; Hollenbeck, Brent K; Ghani, Khurshid R

2015-05-01

To examine the use of social media within Endourology by reporting on its utilization during the 2013 World Congress of Endourology (WCE) annual meeting. Two social media platforms were analyzed for this study: Twitter (San Francisco, CA) and LinkedIn (Mountain View, CA). For Twitter, a third-party analysis service (Tweetreach) was used to quantitatively analyze all tweets with the hashtags #WCE2013 and #WCE13 during a 7-day period surrounding the WCE. Two reviewers independently classified tweet content using a predefined Twitter-specific classification system. Tweet sentiment was determined using sentiment analysis software (Semantria, Inc., Amherst, MA). Finally, the penetration of Twitter and LinkedIn within the WCE faculty was assessed by means of a manual search. During the study period, 335 tweets had the hashtag #WCE2013 or #WCE13. Content originated from 68 users resulting in a mean of 47 tweets/day and 4.9 tweets/contributor. Conference-related tweets had a reach of 38,141 unique Twitter accounts and an online exposure of 188,629 impressions. Physicians generated the majority of the content (63%), of which 55.8% were not attending the meeting. More tweets were informative (56.7%) versus uninformative (43.3%), and 17.9% had links to an external web citation. The mean sentiment score was 0.13 (range -0.90 to 1.80); 13.1%, 57.0%, and 29.9% of tweets were negative, neutral, and positive in sentiment, respectively. Of 302 WCE meeting faculty, 150 (49.7%) had registered LinkedIn accounts while only 52 (17.2%) had Twitter accounts, and only 19.2% tweeted during the meeting. Despite a relatively low number of Twitter users, tweeting about the WCE meeting dramatically increased its online exposure with dissemination of content that was mostly informative including engagement with physicians not attending the conference. While half of faculty at WCE 2013 had LinkedIn accounts, their social media footprint in Twitter was limited.

Twitter for travel medicine providers.

Science.gov (United States)

Mills, Deborah J; Kohl, Sarah E

2016-03-01

Travel medicine practitioners, perhaps more so than medical practitioners working in other areas of medicine, require a constant flow of information to stay up-to-date, and provide best practice information and care to their patients. Many travel medicine providers are unaware of the popularity and potential of the Twitter platform. Twitter use among our travellers, as well as by physicians and health providers, is growing exponentially. There is a rapidly expanding body of published literature on this information tool. This review provides a brief overview of the ways Twitter is being used by health practitioners, the advantages that are peculiar to Twitter as a platform of social media, and how the interested practitioner can get started. Some key points about the dark side of Twitter are highlighted, as well as the potential benefits of using Twitter as a way to disseminate accurate medical information to the public. This article will help readers develop an increased understanding of Twitter as a tool for extracting useful facts and insights from the ever increasing volume of health information. © International Society of Travel Medicine, 2016. All rights reserved. Published by Oxford University Press. For permissions, please e-mail: journals.permissions@oup.com.

Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

Science.gov (United States)

Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

2018-04-01

There are many ways of implementing the use of sentiments often found in documents; one of which is the sentiments found on the product or service reviews. It is so important to be able to process and extract textual data from the documents. Therefore, we propose a system that is able to classify sentiments from review documents into two classes: positive sentiment and negative sentiment. We use Naive Bayes Classifier method in this document classification system that we build. We choose Movienthusiast, a movie reviews in Bahasa Indonesia website as the source of our review documents. From there, we were able to collect 1201 movie reviews: 783 positive reviews and 418 negative reviews that we use as the dataset for this machine learning classifier. The classifying accuracy yields an average of 88.37% from five times of accuracy measuring attempts using aforementioned dataset.

Affective topic model for social emotion detection.

Science.gov (United States)

Rao, Yanghui; Li, Qing; Wenyin, Liu; Wu, Qingyuan; Quan, Xiaojun

2014-10-01

The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's perspective, analysis from the reader's perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a reader's emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed.

Science.gov (United States)

Williams, Shirley Ann; Terras, Melissa; Warwick, Claire

2013-01-01

Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper's title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect. As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge

How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed

Science.gov (United States)

2013-01-01

Background Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. Objective This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Methods Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect. Results As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that

Twitter for Libraries (and Librarians)

Science.gov (United States)

Milstein, Sarah

2009-01-01

For many people, the word "twitter" brings to mind birds rather than humans. But information professionals know that Twitter (www.twitter.com) is a fast-growing, free messaging service for people, and it's one that libraries (and librarians) can make good use of--without spending much time or effort. This article discusses the many potential uses…

Assessing Anti-American Sentiment Through Social Media Analysis

Science.gov (United States)

2016-12-01

165 Yuri Kageyama, “Twitter Takes Off in Japan – Social Network Changes Nation’s Internet Culture,” Journal - Gazette, July 4, 2010, http...military shows of force in and around Japan . 14. SUBJECT TERMS anti-Americanism, Twitter, social media analysis; drone strike; Pakistan, UAV...hypothesizes that social media analysis, specifically analysis of messages sent through the Twitter network , can be used to gauge the overall

Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control.

Science.gov (United States)

Salathé, Marcel; Khandelwal, Shashank

2011-10-01

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.

Understanding the Demographics of Twitter Users

DEFF Research Database (Denmark)

Mislove, Alan; Jørgensen, Sune Lehmann; Ahn, Yong-Yeol

2011-01-01

Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself......: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter...... population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population....

On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

Directory of Open Access Journals (Sweden)

Asriyanti Indah Pratiwi

2018-01-01

Full Text Available Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.

Economic sentiment indicator and its information capability in the Czech Republic

Directory of Open Access Journals (Sweden)

Radka Martináková

2013-01-01

Full Text Available The paper focuses on the indicators of economic agents’ perceptions in the Czech Republic. We assume that these information are provided by economic sentiment indicator surveys based on the Joint Harmonised EU Programme. The aim of this paper is to offer the alternate methodology of qualitative data transformation (balance statistic data in relation with the macroeconomic quantitative indicators. In the empirical analysis we distinguished between the indicators of confidence in industry, construction, retail and consumer confidence indicator. We found link between the aggregate economic sentiment indicator and economic activity. Especially, aggregate economic sentiment indicator copies the development of the GDP. However, partial indicators does not follow changes in the specific sectors of the economy. We also found that economic agents underestimate the intensity of the economic recession after the year 2007.Finally, we cannot recommend the economic sentiment indicator as the leading indicator of the future economic activity in the Czech Republic. Our methodological contribution is in quantifying of the consumer survey results by standardization.

Electronic word of mouth on twitter about physical activity in the United States: exploratory infodemiology study.

Science.gov (United States)

Zhang, Ni; Campo, Shelly; Janz, Kathleen F; Eckler, Petya; Yang, Jingzhen; Snetselaar, Linda G; Signorini, Alessio

2013-11-20

Twitter is a widely used social medium. However, its application in promoting health behaviors is understudied. In order to provide insights into designing health marketing interventions to promote physical activity on Twitter, this exploratory infodemiology study applied both social cognitive theory and the path model of online word of mouth to examine the distribution of different electronic word of mouth (eWOM) characteristics among personal tweets about physical activity in the United States. This study used 113 keywords to retrieve 1 million public tweets about physical activity in the United States posted between January 1 and March 31, 2011. A total of 30,000 tweets were randomly selected and sorted based on numbers generated by a random number generator. Two coders scanned the first 16,100 tweets and yielded 4672 (29.02%) tweets that they both agreed to be about physical activity and were from personal accounts. Finally, 1500 tweets were randomly selected from the 4672 tweets (32.11%) for further coding. After intercoder reliability scores reached satisfactory levels in the pilot coding (100 tweets separate from the final 1500 tweets), 2 coders coded 750 tweets each. Descriptive analyses, Mann-Whitney U tests, and Fisher exact tests were performed. Tweets about physical activity were dominated by neutral sentiments (1270/1500, 84.67%). Providing opinions or information regarding physical activity (1464/1500, 97.60%) and chatting about physical activity (1354/1500, 90.27%) were found to be popular on Twitter. Approximately 60% (905/1500, 60.33%) of the tweets demonstrated users' past or current participation in physical activity or intentions to participate in physical activity. However, social support about physical activity was provided in less than 10% of the tweets (135/1500, 9.00%). Users with fewer people following their tweets (followers) (P=.02) and with fewer accounts that they followed (followings) (P=.04) were more likely to talk positively about

NOTE FOR EDITOR: Twitter As An Educational Environment

OpenAIRE

Selami AYDIN

2014-01-01

The purpose of the study is to present a review of Twitter as an educational environment, as research is relatively new. The reviewed studies have been categorized into three sections: Ø Reasons to use Twitter, Ø Twitter as an educational environment, and Ø some drawbacks. Twitter and language teaching and learning and Twitter and libraries were subtitled under the section of Twitter as an educational environment. To conclude, current literature reflects that Twitter has a positi...

New Literacies Practices of Teenage "Twitter" Users

Science.gov (United States)

Gleason, Benjamin

2016-01-01

This study is an empirical study into the new literacy practices of five teenage "Twitter" users on Twitter. Qualitative methods were used to describe the most prominent ways of participating on "Twitter." Results indicate that teenagers used "Twitter" for self-expression, communication, friendship maintenance, and…

Twitter Finder

OpenAIRE

Gil Blazquez, Lander

2016-01-01

La aplicación web a desarrollar se llama Twitter Finder. Se trata de una página web en la que cabe destacar como partes más importantes un buscador y un mapa. El usuario podrá hacer búsquedas de una o varias palabras clave a través del buscador. Con la búsqueda realizada y con la ayuda de la API de Twitter, se obtendrán los últimos tweets escritos que contengan el texto de la búsqueda,almacenándolos en la base de datos.

Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development.

Science.gov (United States)

Hart, Mark; Stetten, Nichole E; Islam, Sabrina; Pizarro, Katherine

2017-09-20

The use of social networking sites is increasingly being adopted in public health, in part, because of the barriers to funding and reduced resources. Public health professionals are using social media platforms, specifically Twitter, as a way to facilitate professional development. The objective of this study was to identify public health professionals using Twitter and to analyze how they use this platform to enhance their formal and informal professional development within the context of public health. Keyword searches were conducted to identify and invite potential participants to complete a survey related to their use of Twitter for public health and professional experiences. Data regarding demographic attributes, Twitter usage, and qualitative information were obtained through an anonymous Web-based survey. Open-response survey questions were analyzed using the constant comparison method. "Using Twitter makes it easier to expand my networking opportunities" and "I find Twitter useful for professional development" scored highest, with a mean score of 4.57 (standard deviation [SD] 0.74) and 4.43 (SD 0.76) on a 5-point Likert scale. Analysis of the qualitative data shows the emergence of the following themes for why public health professionals mostly use Twitter: (1) geography, (2) continuing education, (3) professional gain, and (4) communication. For public health professionals in this study, Twitter is a platform best used for their networking and professional development. Furthermore, the use of Twitter allows public health professionals to overcome a series of barriers and enhances opportunities for growth. ©Mark Hart, Nichole E Stetten, Sabrina Islam, Katherine Pizarro. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 20.09.2017.

Investor sentiment, mutual fund flows and its impact on returns and volatility

NARCIS (Netherlands)

Lehnert, T.; Müller, A.; Frijns, B.P.M.; Beaumont, R.J.; Daele, M. van

2008-01-01

- Purpose – The purpose of this paper is to investigate the impact of individual investor sentiment on the return process and conditional volatility of three main US market indices (Dow Jones Industrial Average, S&P500 and Nasdaq100). Individual investor sentiment is measured by aggregate money

Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak.

Directory of Open Access Journals (Sweden)

Cynthia Chew

Full Text Available BACKGROUND: Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary "infoveillance" approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1 monitor the use of the terms "H1N1" versus "swine flu" over time; 2 conduct a content analysis of "tweets"; and 3 validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool. METHODOLOGY/PRINCIPAL FINDINGS: Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords "swine flu," "swineflu," and/or "H1N1." using Infovigil, an infoveillance system. Tweets using "H1N1" increased from 8.8% to 40.5% (R(2 = .788; p<.001, indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%. 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%, while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data. CONCLUSIONS: This study illustrates the potential of using social media to conduct "infodemiology" studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.

The Asymmetric Effects of Investor Sentiment

DEFF Research Database (Denmark)

Lutz, Chandler

investors only act as corrective force during certain time periods. We also show that our index predicts implied volatility, media pessimism, and mutual fund flows. Overall, our findings are consistent with both the theories and anecdotal accounts of investor sentiment in the stock market....

A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

Science.gov (United States)

Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

2018-03-01

Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

The Use of Twitter by the Trauma and Orthopaedic Surgery Journals: Twitter Activity, Impact Factor, and Alternative Metrics.

Science.gov (United States)

Hughes, Hannah; Hughes, Andrew; Murphy, Colin

2017-12-10

Aim Social media (SoMe) platforms have become leading methods of communication and dissemination of scientific information in the medical community. They allow for immediate discussion and widespread engagement around important topics. It has been hypothesized that the activity on Twitter positively correlates with highly cited articles. The purpose of this study was to analyze the prevalence and activity of Trauma and Orthopaedic Surgery journals on Twitter, with the hypothesis that the impact factor is positively associated with the Twitter usage. Methods The top 50 Trauma and Orthopaedic Surgery journals, ranked by 2016 Impact Factor were analyzed. The Twitter profiles of each journal or affiliated society were identified. Other SoMe platforms used were also recorded. The Twitonomy software (Digonomy Pty Ltd, New South Wales, Australia) was used to analyze the Twitter profiles over a one-year period. The Twitter Klout scores were recorded for each journal to approximate the SoMe influence. The Altmetric scores (the total number of mentions via alternative metrics) were also recorded. The statistical analysis was carried out to identify correlations between journal Impact Factors, SoMe activity, Twitter Klout scores and Altmetric scores.  Results Twenty-two journals (44%) were dedicated to the Twitter profiles. Fourteen journals (28%) were associated with societies that had profiles and 14 journals (28%) had no Twitter presence. The mean Impact Factor overall was 2.16 +/- 0.14 (range, 1.07-5.16). The journals with dedicated Twitter profiles had higher Impact Factors than those without (mean 2.41 vs. 1.61; P=0.005). A greater number of Twitter followers were associated with higher Impact Factors (R2 0.317, P=0.03). The journals with higher Twitter Klout scores had higher Impact Factors (R2 0.357, P=0.016). The Altmetric score was positively associated with an Impact Factor (R2 0.310, P=0.015). The journals with higher numbers of retweets (virtual citations in

Ranking product aspects through sentiment analysis of online reviews

Science.gov (United States)

Wang, Wei; Wang, Hongwei; Song, Yuan

2017-03-01

The electronic word-of-mouth (e-WOM) is one of the most important among all the factors affecting consumers' behaviours. Opinions towards a product through online reviews will influence purchase decisions of other online consumers by changing their perceptions on the product quality. Furthermore, each product aspect may impact consumers' intentions differently. Thus, sentiment analysis and econometric models are incorporated to examine the relationship between purchase intentions and aspect-opinion pairs, which enable the weight estimation for each product aspect. We first identify product aspects and reduce dimensions to extract aspect-opinion pairs. Next the information gain is calculated for each aspect through entropy theory. Based on sentiment polarity and sentiment strength, we formulate an econometric model by integrating the information gain to measure the aspect's weight. In the experiment, we track 386 digital cameras on Amazon for 39 months, and results show that the aspect weight for digital cameras is detected more precisely than TF-ID and HAC algorithms. The results will bridge product aspects and consumption intention to facilitate e-WOM-based marketing.

Using Twitter data for demographic research

Directory of Open Access Journals (Sweden)

Dilek Yildiz

2017-11-01

Full Text Available Background: Social media data is a promising source of social science data. However, deriving the demographic characteristics of users and dealing with the nonrandom, nonrepresentative populations from which they are drawn represent challenges for social scientists. Objective: Given the growing use of social media data in social science research, this paper asks two questions: 1 To what extent are findings obtained with social media data generalizable to broader populations, and 2 what is the best practice for estimating demographic information from Twitter data? Methods: Our analyses use information gathered from 979,992 geo-located Tweets sent by 22,356 unique users in South East England between 23 June and 4 July 2014. We estimate demographic characteristics of the Twitter users with the crowd-sourcing platform CrowdFlower and the image-recognition software Face++. To evaluate bias in the data, we run a series of log-linear models with offsets and calibrate the nonrepresentative sample of Twitter users with mid-year population estimates for South East England. Results: CrowdFlower proves to be more accurate than Face++ for the measurement of age, whereas both tools are highly reliable for measuring the sex of Twitter users. The calibration exercise allows bias correction in the age-, sex-, and location-specific population counts obtained from the Twitter population by augmenting Twitter data with mid-year population estimates. Contribution: The paper proposes best practices for estimating Twitter users' basic demographic characteristics and a calibration method to address the selection bias in the Twitter population, allowing researchers to generalize findings based on Twitter to the general population.

The Impact of Investors¡¯ Sentiment on the Equity Market: Evidence from Ghanaian Stock Market

OpenAIRE

Ebenezer Bennet; Lydia Obenewaa Amoako; Ricky Okine Charles; Asumadu Edward; Joseph Asante Darkwah

2012-01-01

Investor¡¯s Sentiment plays a major role in choosing which stocks we invest. Investors¡¯ sentiment can be defined as investors¡¯ attitude and opinion towards investing in the Stocks. The aim of this research is to analyse the individual investor¡¯s sentiment and also to analyse the influence of Market Specific Factors on investors¡¯ sentiment. The investor¡¯s attitude towards investing is influenced by rumours, intuition, herd behaviour among investors and media coverage of the stock. 100 inv...

An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets.

Science.gov (United States)

Rocheleau, Mary; Sadasivam, Rajani Shankar; Baquis, Kate; Stahl, Hannah; Kinney, Rebecca L; Pagoto, Sherry L; Houston, Thomas K

2015-01-14

Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers. Our aim was to examine current Twitter activity for smoking cessation. Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational). The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the

Analysis of emergency physicians' Twitter accounts.

Science.gov (United States)

Lulic, Ileana; Kovic, Ivor

2013-05-01

Twitter is one of the fastest growing social media networks for communication between users via short messages. Technology proficient physicians have demonstrated enthusiasm in adopting social media for their work. To identify and create the largest directory of emergency physicians on Twitter, analyse their user accounts and reveal details behind their connections. Several web search tools were used to identify emergency physicians on Twitter with biographies completely or partially written in English. NodeXL software was used to calculate emergency physicians' Twitter network metrics and create visualisation graphs. The authors found 672 Twitter accounts of self-identified emergency physicians. Protected accounts were excluded from the study, leaving 632 for further analysis. Most emergency physicians were located in USA (55.4%), had created their accounts in 2009 (43.4%), used their full personal name (77.5%) and provided a custom profile picture (92.2%). Based on at least one published tweet in the last 15 days, there were 345 (54.6%) active users on 31 December 2011. Active users mostly used mobile devices based on the Apple operating system to publish tweets (69.2%). Visualisation of emergency physicians' Twitter network revealed many users with no connections with their colleagues, and a small group of most influential users who were highly interconnected. Only a small proportion of registered emergency physicians use Twitter. Among them exists a smaller inner network of emergency physicians with strong social bonds that is using Twitter's full potentials for professional development.

Changes and Sentiment: A Longitudinal E-Mail Analysis of a Large Design Project

DEFF Research Database (Denmark)

Piccolo, Sebastiano; Wilberg, Julian; Lindemann, Udo

" in emails and study its relation to sentiment. We find that sentiment decreases when problems or changes emerge, and increases when changes are implemented successfully. We discuss the implications of our findings for research and project engineering practice, providing avenues for further work....

Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

DEFF Research Database (Denmark)

Hillmann, Robert; Trier, Matthias

2012-01-01

. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks...... with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social...... networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only...

Using Social Media to Measure Student Wellbeing: A Large-Scale Study of Emotional Response in Academic Discourse

Energy Technology Data Exchange (ETDEWEB)

Volkova, Svitlana; Han, Kyungsik; Corley, Courtney D.

2016-11-15

Student resilience and emotional well-being are essential for both academic and social development. Earlier studies on tracking students' happiness in academia showed that many of them struggle with mental health issues. For example, a 2015 study at the University of California Berkeley found that 47% of graduate students suffer from depression, following a 2005 study that showed 10% had considered suicide. This is the first large-scale study that uses signals from social media to evaluate students' emotional well-being in academia. This work presents fine-grained emotion and opinion analysis of 79,329 tweets produced by students from 44 universities. The goal of this study is to qualitatively evaluate and compare emotions and sentiments emanating from students' communications across different academic discourse types and across universities in the U.S. We first build novel predictive models to categorize academic discourse types generated by students into personal, social, and general categories. We then apply emotion and sentiment classification models to annotate each tweet with six Ekman's emotions -- joy, fear, sadness, disgust, anger, and surprise and three opinion types -- positive, negative, and neutral. We found that emotions and opinions expressed by students vary across discourse types and universities, and correlate with survey-based data on student satisfaction, happiness and stress. Moreover, our results provide novel insights on how students use social media to share academic information, emotions, and opinions that would pertain to students academic performance and emotional well-being.

Multi-class machine classification of suicide-related communication on Twitter.

Science.gov (United States)

Burnap, Pete; Colombo, Gualtiero; Amery, Rosie; Hodorog, Andrei; Scourfield, Jonathan

2017-08-01

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type.

NRFixer: Sentiment Based Model for Predicting the Fixability of Non-Reproducible Bugs

Directory of Open Access Journals (Sweden)

Anjali Goyal

2017-08-01

Full Text Available Software maintenance is an essential step in software development life cycle. Nowadays, software companies spend approximately 45\\% of total cost in maintenance activities. Large software projects maintain bug repositories to collect, organize and resolve bug reports. Sometimes it is difficult to reproduce the reported bug with the information present in a bug report and thus this bug is marked with resolution non-reproducible (NR. When NR bugs are reconsidered, a few of them might get fixed (NR-to-fix leaving the others with the same resolution (NR. To analyse the behaviour of developers towards NR-to-fix and NR bugs, the sentiment analysis of NR bug report textual contents has been conducted. The sentiment analysis of bug reports shows that NR bugs' sentiments incline towards more negativity than reproducible bugs. Also, there is a noticeable opinion drift found in the sentiments of NR-to-fix bug reports. Observations driven from this analysis were an inspiration to develop a model that can judge the fixability of NR bugs. Thus a framework, {NRFixer,} which predicts the probability of NR bug fixation, is proposed. {NRFixer} was evaluated with two dimensions. The first dimension considers meta-fields of bug reports (model-1 and the other dimension additionally incorporates the sentiments (model-2 of developers for prediction. Both models were compared using various machine learning classifiers (Zero-R, naive Bayes, J48, random tree and random forest. The bug reports of Firefox and Eclipse projects were used to test {NRFixer}. In Firefox and Eclipse projects, J48 and Naive Bayes classifiers achieve the best prediction accuracy, respectively. It was observed that the inclusion of sentiments in the prediction model shows a rise in the prediction accuracy ranging from 2 to 5\\% for various classifiers.

A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes.

Science.gov (United States)

Ghassemi, Mohammad M; Mark, Roger G; Nemati, Shamim

2015-09-01

Our objective in this paper was to visualize the evolution of clinical language and sentiment with respect to several common population-level categories including: time in the hospital, age, mortality, gender and race. Our analysis utilized seven years of unstructured free text notes from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. The text data was partitioned by category and used to generate several high dimensional vector space representations. We generated visualizations of the vector spaces using Distributed Stochastic Neighbor Embedding (tSNE) and Principal Component Analysis (PCA). We also investigated representative words from clusters in the vector space. Lastly, we inferred the general sentiment of the clinical notes toward each parameter by gauging the average distance between positive and negative keywords and all other terms in the space. We found intriguing differences in the sentiment of clinical notes over time, outcome, and demographic features. We noted a decrease in the homogeneity and complexity of clusters over time for patients with poor outcomes. We also found greater positive sentiment for females, unmarried patients, and patients of African ethnicity.

Emergent Collaboration on Twitter

DEFF Research Database (Denmark)

Lundgaard, Daniel; Razmerita, Liana; Tan, Chee-Wee

2018-01-01

This paper explores the organizing elements that foster emergent collaboration within large-scale communities on online social platforms like Twitter. This study is based on a case study of the #BlackLivesMatter social movement and draws on organizing dynamics and online social network literature...... foster emergent collaboration in social movements using Twitter....

Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

KAUST Repository

Salas-Zárate, María del Pilar

2017-04-24

In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.

Automatic Detection of Satire in Twitter: A psycholinguistic-based approach

KAUST Repository

Salas-Zá rate, Marí a del Pilar; Paredes-Valverde, Mario André s; Rodriguez-Garcia, Miguel Angel; Valencia-Garcí a, Rafael; Alor-Herná ndez, Giner

2017-01-01

In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire.

The predictive power of the business and bank sentiment of firms : A high-dimensional Granger causality approach

NARCIS (Netherlands)

Wilms, I.; Gelper, S.E.C.; Croux, C.

2016-01-01

We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector – their main credit providers. The use of

Funciones de la novela sentimental hispanoamericana durante el siglo XIX

OpenAIRE

Zó, Ramiro

2007-01-01

Se analizará cómo funcionan los textos sentimentales hispanoamericanos en los lectores según dos modos de concebir la recepción de estas obras: la lectura en un “microambiente" de un lector/a sentimental-individual y la lectura en un “macroambiente" de varios lectores de novelas sentimentales. Así también, se pretende fijar las características fundamentales de la narrativa sentimental hispanoamericana en un conjunto de textos representativos de esta modalidad literaria. A su vez, este análisi...

Twitter and the Cyberpolitics

OpenAIRE

Fernández, Carmen Beatriz

2012-01-01

Este ensayo explora el rol que viene asumiendo Twitter en la ciberpolítica de la región, con base en estudios y data recuiente. Twitter día a día bate sus propios records con 200 millones de cuentas en todo el mundo y 140 millones de tuits al día, crece a una vertiginosa tasa de 500 mil nuevos usuarios al día. ¿Es Twitter un buen medio para la comunicación política? ¿es un medio útil para los políticos? ¿y para los ciudadanos? La respuesta es sólo una, y enfática: sí. En cualquier campaña es ...

"Emotion": The History of a Keyword in Crisis.

Science.gov (United States)

Dixon, Thomas

2012-10-01

The word "emotion" has named a psychological category and a subject for systematic enquiry only since the 19th century. Before then, relevant mental states were categorised variously as "appetites," "passions," "affections," or "sentiments." The word "emotion" has existed in English since the 17th century, originating as a translation of the French émotion , meaning a physical disturbance. It came into much wider use in 18th-century English, often to refer to mental experiences, becoming a fully fledged theoretical term in the following century, especially through the influence of two Scottish philosopher-physicians, Thomas Brown and Charles Bell. This article relates this intellectual and semantic history to contemporary debates about the usefulness and meaning of "emotion" as a scientific term.

Enhance Your Twitter Experience

Science.gov (United States)

Miller, Shannon McClintock

2010-01-01

The author has been encouraging teachers, students, and others to join Twitter and build their personal learning networks (PLNs) ever since she delved into this great social networking site. In this article, she offers a few other tools and tips that can improve the Twitter experience of those who have opened up an account and dabbled a bit but…

What Can Rational Investors Do About Excessive Volatility and Sentiment Fluctuations?

OpenAIRE

Bernard Dumas; Alexander Kurshev; Raman Uppal

2005-01-01

Our objective is to understand the trading strategy that would allow an investor to take advantage of 'excessive' stock price volatility and 'sentiment' fluctuations. We construct a general equilibrium model of sentiment. In it, there are two classes of agents and stock prices are excessively volatile because one class is overconfident about a public signal. As a result, this class of irrational agents changes its expectations too often, sometimes being excessively optimistic, sometimes being...

A Survey on Sentiment Classification in Face Recognition

Science.gov (United States)

Qian, Jingyu

2018-01-01

Face recognition has been an important topic for both industry and academia for a long time. K-means clustering, autoencoder, and convolutional neural network, each representing a design idea for face recognition method, are three popular algorithms to deal with face recognition problems. It is worthwhile to summarize and compare these three different algorithms. This paper will focus on one specific face recognition problem-sentiment classification from images. Three different algorithms for sentiment classification problems will be summarized, including k-means clustering, autoencoder, and convolutional neural network. An experiment with the application of these algorithms on a specific dataset of human faces will be conducted to illustrate how these algorithms are applied and their accuracy. Finally, the three algorithms are compared based on the accuracy result.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

Directory of Open Access Journals (Sweden)

Young Bin Kim

Full Text Available In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs. Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

Science.gov (United States)

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496

Does Sentiment Among Users in Online Social Networks Polarize or Balance Out?

DEFF Research Database (Denmark)

Trier, Matthias; Hillmann, Robert

2017-01-01

Users express and share sentiments electronically when they communicate within online social network applications. One way to analyze such interdependent data is focusing on the inter-user relationships by applying a sociological perspective based on social network analysis. Existing studies exam...... examined the existence or distribution of sentiments in online communication at a general level or in small observed groups....

Sentiments that Affect Sociopolitical Legitimation of TNCs in Bangladesh, India, and Pakistan

DEFF Research Database (Denmark)

Bakhtiar Rana, Mohammad; Sørensen, Olav Jull

2014-01-01

Our study investigates the transnational enterprises' (TNCs’) socio-political legitimization in Bangladesh, India, and Pakistan with a view to ensuring sustainability in the institutional context. We took institutional perspective as a theoretical lens, used a grounded theory method, and employed...... and patriotism,’ and ‘ecological balance’, and suggest that tensions stemming from these three sentiments can be managed and often turned into opportunities if appropriate strategies are applied. Eight types of strategies derived from the nine case studies are presented in order to mitigate, manage, and cash...... in on the REN-sentiments in South Asian markets. These strategies include collaboration strategy, local development strategy, strategy of alignment with socio-political actors, local name and staffing strategy, hibernation strategy, sentiment-focused strategy, isomorphism strategy, and openness strategy....

Mapping auroral activity with Twitter

Science.gov (United States)

Case, N. A.; MacDonald, E. A.; Heavner, M.; Tapia, A. H.; Lalone, N.

2015-05-01

Twitter is a popular, publicly accessible, social media service that has proven useful in mapping large-scale events in real time. In this study, for the first time, the use of Twitter as a measure of auroral activity is investigated. Peaks in the number of aurora-related tweets are found to frequently coincide with geomagnetic disturbances (detection rate of 91%). Additionally, the number of daily aurora-related tweets is found to strongly correlate with several auroral strength proxies (ravg≈0.7). An examination is made of the bias for location and time of day within Twitter data, and a first-order correction of these effects is presented. Overall, the results suggest that Twitter can provide both specific details about an individual aurora and accurate real-time indication of when, and even from where, an aurora is visible.

The relationship between SERVQUAL, national customer satisfaction indices & consumer sentiment

DEFF Research Database (Denmark)

Kristensen, Kai; Eskildsen, Jacob Kjær

2008-01-01

The focus of this study is to integrate SERVQUAL with a national customer satisfaction index in this context the EPSI Rating framework and explore the possible relationship with consumer sentiment measures. The data for this study comes from the Danish Customer Satisfaction Index 2007. Here app....... 1700 customers have evaluated their preferred bank. The questionnaire consists of two parts: the basic EPSI statement as well as 15 statements covering the 5 dimensions from SERVQUAL. Furthermore the respondents answered two questions related to consumer sentiment. The results show that both SERVQUAL...

Stigma Sentiments and Self-Meanings: Exploring the Modified Labeling Theory of Mental Illness

Science.gov (United States)

Kroska, Amy; Harkness, Sarah K.

2006-01-01

We introduce "stigma sentiments" as a way to operationalize the cultural conceptions of the mentally ill. Stigma sentiments are the evaluation, potency, and activity (EPA) associated with the cultural category "a mentally ill person." We find consistent support for the validity of the evaluation and potency components as measures of these…

Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data

Science.gov (United States)

King, Kylie; Turnure, Jackie; Sukunesan, Suku; Phelps, Andrea; Pirkis, Jane

2018-01-01

Background It has been suggested that some dominant aspects of traditional masculinity are contributing to the high suicide rates among Australian men. We developed a three-episode documentary called Man Up, which explores the complex relationship between masculinity and suicide and encourages men to question socially imposed rules about what it means to be a man and asks them to open up, express difficult emotions, and seek help if and when needed. We ran a three-phase social media campaign alongside the documentary using 5 channels (Twitter, Facebook, Instagram, YouTube, and Tumblr). Objective This study aimed to examine the extent to which the Man Up Twitter campaign influenced the social media conversation about masculinity and suicide. Methods We used Twitter insights data to assess the reach of and engagement with the campaign (using metrics on followers, likes, retweets, and impressions) and to determine the highest and lowest performing tweets in the campaign (using an aggregated performance measure of reactions). We used original content tweets to determine whether the campaign increased the volume of relevant Twitter conversations (aggregating the number of tweets for selected campaign hashtags over time), and we used a subset of these data to gain insight into the main content themes with respect to audience engagement. Results The campaign generated a strong following that was engaged with the content of the campaign; over its whole duration, the campaign earned approximately 5000 likes and 2500 retweets and gained around 1,022,000 impressions. The highest performing tweets posted by the host included video footage and occurred during the most active period of the campaign (around the screening of the documentary). The volume of conversations in relation to commonly used hashtags (#MANUP, #ABCMANUP, #LISTENUP, and #SPEAKUP) grew in direct relation to the campaign activities, achieving strongest growth during the 3 weeks when the documentary was aired

How to normalize Twitter counts? A first attempt based on journals in the Twitter Index.

Science.gov (United States)

Bornmann, Lutz; Haunschild, Robin

One possible way of measuring the broad impact of research (societal impact) quantitatively is the use of alternative metrics (altmetrics). An important source of altmetrics is Twitter, which is a popular microblogging service. In bibliometrics, it is standard to normalize citations for cross-field comparisons. This study deals with the normalization of Twitter counts (TC). The problem with Twitter data is that many papers receive zero tweets or only one tweet. In order to restrict the impact analysis on only those journals producing a considerable Twitter impact, we defined the Twitter Index (TI) containing journals with at least 80 % of the papers with at least 1 tweet each. For all papers in each TI journal, we calculated normalized Twitter percentiles (TP) which range from 0 (no impact) to 100 (highest impact). Thus, the highest impact accounts for the paper with the most tweets compared to the other papers in the journal. TP are proposed to be used for cross-field comparisons. We studied the field-independency of TP in comparison with TC. The results point out that the TP can validly be used particularly in biomedical and health sciences, life and earth sciences, mathematics and computer science, as well as physical sciences and engineering. In a first application of TP, we calculated percentiles for countries. The results show that Denmark, Finland, and Norway are the countries with the most tweeted papers (measured by TP).

Twitter and the Cyberpolitics

Directory of Open Access Journals (Sweden)

Carmen Beatriz Fernández

2012-02-01

Full Text Available This paper explores the role that Twitter is taking in the e-policy of the region, based on recent studies and data. Twitter every day beats its own record with 200 million accounts worldwide and 140 million tweets per day, growing at a dizzying rate of 500 thousand new users per day. Is Twitter a good medium for political communication? Is it a useful tool for politicians? What about the citizens? The answer is only one, and emphatic yes. In any campaign should provide demonstrations of numerical strength. There is an important segment of the electorate based on the manifestations of power, and predictions of victory to finally make their voting decision. The article explores current uses, trends and contrasts with traditional media.

‘Thousands of throbbing hearts' - Sentimentality and community in popular Victorian poetry: Longfellow's Evangeline and Tennyson's Enoch Arden

Directory of Open Access Journals (Sweden)

Kirstie Blair

2007-04-01

Full Text Available This essay explores the function of sentimentality in popular nineteenth-century narrative poetry by focusing on Tennyson's 'Enoch Arden' and Longfellow's 'Evangeline', two poems that have suffered relative critical neglect due to their status as sentimental verse. It argues that both texts, in their stories of exile, alienation and eventual recuperation, set up their hero and heroine as role-models for ways of feeling and use them to examine the possibility of using personal feeling as a conduit for communal sentiment. While both poems deploy the standard tropes of Victorian sentimentality, the ambiguous conclusions of 'Enoch Arden 'and 'Evangeline' , I argue, call into question the clichés of sentimental discourse. The fates of Enoch and of Evangeline offer, to some extent, a darker vision of the potential for sentimental responses to an individual's suffering to create feeling communities either within or without the poem.

Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

OpenAIRE

Twanabasu, Bikesh

2018-01-01

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiment...

Arabic Feature-Based Level Sentiment Analysis Using Lexicon ...

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... structured reviews being prior knowledge for mining unstructured reviews. ... FDSO has been introduced, which defines a space of product features ... polarity of a review using feature ontology and sentiment lexicons.

360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet

Directory of Open Access Journals (Sweden)

Eleanor Mulholland

2015-08-01

Full Text Available Online recommender systems are useful for media asset management where they select the best content from a set of media assets. We have developed an architecture for 360-MAM- Select, a recommender system for educational video content. 360-MAM-Select will utilise sentiment analysis and gamification techniques for the recommendation of media assets. 360-MAM-Select will increase user participation with digital content through improved video recommendations. Here, we discuss the architecture of 360-MAM-Select and the use of the Google Prediction API and EmoSenticNet for 360-MAM-Affect, 360-MAM-Select's sentiment analysis module. Results from testing two models for sentiment analysis, Sentiment Classifier (Google Prediction API and EmoSenticNetClassifer (Google Prediction API + EmoSenticNet are promising. Future work includes the implementation and testing of 360-MAM-Select on video data from YouTube EDU and Head Squeeze.

External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews?

NARCIS (Netherlands)

Wijnhoven, Alphonsus B.J.M.; Bloemen, Oscar

2014-01-01

Many publications in sentiment mining provide new techniques for improved accuracy in extracting features and corresponding sentiments in texts. For the external validity of these sentiment reports, i.e., the applicability of the results to target audiences, it is important to well analyze data of

The Asymmetric Predictive Effects of Investor Sentiment

DEFF Research Database (Denmark)

Lutz, Chandler

investors only act as corrective force during certain time periods. We also show that our index predicts implied volatility, media pessimism, and mutual fund flows. Overall, our findings are consistent with both the theories and anecdotal accounts of investor sentiment in the stock market....

Sentiment classification with interpolated information diffusion kernels

NARCIS (Netherlands)

Raaijmakers, S.

2007-01-01

Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of

On an emotional node: modeling sentiment in graphs of action verbs

DEFF Research Database (Denmark)

Petersen, Michael Kai; Hansen, Lars Kai

2012-01-01

Neuroimaging studies have over the past decades established that language is grounded in sensorimotor areas of the brain. Not only action verbs related to face and hand motion but also emotional expressions activate premotor systems in the brain. Hypothesizing that patterns of neural activation...... might be reflected in the latent semantics of words, we apply hierarchical clustering and network graph analysis to quantify the interaction of emotion and motion related action verbs based on two large-scale text corpora. Comparing the word topologies to neural networks we suggest that the co......-activation of associated word forms in the brain resemble the latent semantics of action verbs, which may in turn reflect parameters of force and spatial differentiation underlying action based language....

U.S. State Education Agencies’ Use of Twitter

Directory of Open Access Journals (Sweden)

Yinying Wang

2016-01-01

Full Text Available This study examined how Twitter was used by all U.S. state education agencies (SEAs for public engagement in education. Drawing on the ecological model of communication, this study analyzed the latest 71,913 tweets from 40 SEAs that had official Twitter accounts. The results of correlation analysis indicate no significant relationship between the SEAs’ presence on Twitter and the SEAs’ targeted Twitter users, denoting that the SEAs’ well-intentioned efforts in communicating with stakeholders and the public by using Twitter might fall short of the public’s preferable medium for receiving information. In addition, the results of content analysis suggest that the SEAs primarily used Twitter for one-way asymmetrical information broadcasting, leaving Twitter’s two-way symmetrical communication functionality largely untapped. Findings are discussed with respect to the implications for educational organizations’ effective use of Twitter through the public’s increased participation and collaboration.

Sentiment analysis for PTSD signals

CERN Document Server

Kagan, Vadim; Sapounas, Demetrios

2013-01-01

This book describes a computational framework for real-time detection of psychological signals related to Post-Traumatic Stress Disorder (PTSD) in online text-based posts, including blogs and web forums. Further, it explores how emerging computational techniques such as sentiment mining can be used in real-time to identify posts that contain PTSD-related signals, flag those posts, and bring them to the attention of psychologists, thus providing an automated flag and referral capability.

21 Recipes for Mining Twitter

CERN Document Server

Russell, Matthew

2011-01-01

Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to: Use OAuth to access Twitter dataCreate and analyze graphs of retweet relationshipsUse the streaming API to harvest tweets in realtimeHarvest and analyze friends and followers

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Directory of Open Access Journals (Sweden)

Anang Anggono Lutfi

2018-04-01

Full Text Available The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

Klasifikasi Model Percakapan Twitter Mengenai Ujian Nasional

Directory of Open Access Journals (Sweden)

Emilya Ully Artha

2018-01-01

Full Text Available The rise of internet usage especially in social media such as Twitter make a possibility to analyze an existing conversations and derived it to various conversation theme. The data was obtained from the Twitter conversation using text mining that is a "national exam" using the National #ujian, #unas, and #ujianan hastags. Twitter API was used to extract tweet data. This research use the naive bayesian access method to view communication forms based on users and applications. Based on calculation this research obtain a 0.278 result that most of Twitter users (users are being from @kabarDiknas. The data that used in this research are taken from is a twitter conversation about the “ujian nasional” in 2016.

Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

Directory of Open Access Journals (Sweden)

Ira zulfa

2017-07-01

Full Text Available Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN. Deep Belief Network (DBN, which is included in Deep Learning method, is a stack of several algorithms with some extraction features that optimally utilize all resources. This study has two points. First, it aims to classify positive, negative, and neutral sentiments towards the test data. Second, it determines the classification model accuracy by using Deep Belief Network method so it would be able to be applied into the tweet classification, to highlight the sentiment class of training data tweet in Bahasa Indonesia. Based on the experimental result, it can be concluded that the best method in managing tweet data is the DBN method with an accuracy of 93.31%, compared with  Naive Bayes method which has an accuracy of 79.10%, and SVM (Support Vector Machine method with an accuracy of 92.18%.

Patterns of Twitter Usage in One Cohort-Based Doctoral Program

Directory of Open Access Journals (Sweden)

Armand A Buzzelli

2016-05-01

Full Text Available An Instructional Management and Leadership doctoral program (IML incorporated the use of Twitter to examine what this looked like in practice. Did students actually use Twitter, and if so, how frequently, for what purpose(s, and were there differences between students on the pattern of use? Additionally, we sought to determine if Twitter is a legitimate instructional tool and if the use of Twitter can help mitigate feelings of isolation. Utilizing a descriptive case study design we implemented a survey methodology by distributing a modified version of the First Year Engagement Questionnaire to five IML cohorts. Active use of Twitter was infrequent. IML students used Twitter to gather news, follow experts, and find stimulating interactions. Active users and students who previously had a Twitter account were more positive about using Twitter. On average however, IML students were infrequent, passive Twitter users, aggregating information to supplement instruction. They did not use Twitter to reduce feelings of isolation. Female and male students used Twitter similarly. Younger students were more active than older students. Familiarity with the platform potentially moderates Twitter activity. Twitter has utility as a supplemental instructional tool but expanded use requires active student engagement.

Twitter Fiction: A New Creative Literary Landscape

Directory of Open Access Journals (Sweden)

Laila Al Sharaqi

2016-08-01

Full Text Available Twitter, synonymous with social networking, has become a successful social platform for the exchange of ideas, news, and information. It has also emerged as an experimental platform through which users explore creative realms of poetic and narrative content, albeit in 140 characters. The real-time tweets are fundamentally unique and increasingly sophisticated. The attention deficit generation of the fast-paced contemporary world has little time on its hands for extended discourse. Brief stories have been told throughout human history, however, the popularity of short stories skyrocketed with the advent of digital story telling. Twitter has now become a frontier medium that allows a unique mode of digital storytelling that facilitates creative literary experimentation. Twitter offers a unique freedom to writers insofar as a tweet can be an entire bite-sized story or even a snapshot of a story that requires readers’ active imagination to complete. Twitter fiction signifies stylistic word economy, compactness, symbolic structure, and implied narrative. Fragmentariness of the story is a marker of Twitter fiction. The proponents of Twitter fiction enjoy the originality, freedom, and diversity of perspectives offered by the Twitter fiction. Critics, however, argue that the mandated 140 character limitation stunts story development and strangulates creativity. This paper examines Twitter fiction and proposes that limited characters stories are the evolutionary answer to the reduced attention span of the tech-savvy generation. Keywords: twitterature, fiction, brevity, literary art

Twitter Predicts Citation Rates of Ecological Research.

Science.gov (United States)

Peoples, Brandon K; Midway, Stephen R; Sackett, Dana; Lynch, Abigail; Cooney, Patrick B

2016-01-01

The relationship between traditional metrics of research impact (e.g., number of citations) and alternative metrics (altmetrics) such as Twitter activity are of great interest, but remain imprecisely quantified. We used generalized linear mixed modeling to estimate the relative effects of Twitter activity, journal impact factor, and time since publication on Web of Science citation rates of 1,599 primary research articles from 20 ecology journals published from 2012-2014. We found a strong positive relationship between Twitter activity (i.e., the number of unique tweets about an article) and number of citations. Twitter activity was a more important predictor of citation rates than 5-year journal impact factor. Moreover, Twitter activity was not driven by journal impact factor; the 'highest-impact' journals were not necessarily the most discussed online. The effect of Twitter activity was only about a fifth as strong as time since publication; accounting for this confounding factor was critical for estimating the true effects of Twitter use. Articles in impactful journals can become heavily cited, but articles in journals with lower impact factors can generate considerable Twitter activity and also become heavily cited. Authors may benefit from establishing a strong social media presence, but should not expect research to become highly cited solely through social media promotion. Our research demonstrates that altmetrics and traditional metrics can be closely related, but not identical. We suggest that both altmetrics and traditional citation rates can be useful metrics of research impact.

Twitter predicts citation rates of ecological research

Science.gov (United States)

Peoples, Brandon K.; Midway, Stephen R.; Sackett, Dana K.; Lynch, Abigail; Cooney, Patrick B.

2016-01-01

The relationship between traditional metrics of research impact (e.g., number of citations) and alternative metrics (altmetrics) such as Twitter activity are of great interest, but remain imprecisely quantified. We used generalized linear mixed modeling to estimate the relative effects of Twitter activity, journal impact factor, and time since publication on Web of Science citation rates of 1,599 primary research articles from 20 ecology journals published from 2012–2014. We found a strong positive relationship between Twitter activity (i.e., the number of unique tweets about an article) and number of citations. Twitter activity was a more important predictor of citation rates than 5-year journal impact factor. Moreover, Twitter activity was not driven by journal impact factor; the ‘highest-impact’ journals were not necessarily the most discussed online. The effect of Twitter activity was only about a fifth as strong as time since publication; accounting for this confounding factor was critical for estimating the true effects of Twitter use. Articles in impactful journals can become heavily cited, but articles in journals with lower impact factors can generate considerable Twitter activity and also become heavily cited. Authors may benefit from establishing a strong social media presence, but should not expect research to become highly cited solely through social media promotion. Our research demonstrates that altmetrics and traditional metrics can be closely related, but not identical. We suggest that both altmetrics and traditional citation rates can be useful metrics of research impact.

Developing resources for sentiment analysis of informal Arabic text in social media

OpenAIRE

Itani, Maher; Roast, Chris; Al-Khayatt, Samir

2017-01-01

Natural Language Processing (NLP) applications such as text categorization, machine translation, sentiment analysis, etc., need annotated corpora and lexicons to check quality and performance. This paper describes the development of resources for sentiment analysis specifically for Arabic text in social media. A distinctive feature of the corpora and lexicons developed are that they are determined from informal Arabic that does not conform to grammatical or spelling standards. We refer to Ara...

Reconciliation Sentiment among Victims of Genocide in Rwanda: Conceptualizations, and Relationships with Mental Health

Science.gov (United States)

Mukashema, Immaculee; Mullet, Etienne

2010-01-01

In two studies that were conducted in Rwanda, we have examined the conceptualizations held by people who have experienced genocide with regard to reconciliation sentiment and quantitatively assessed the relationship between reconciliation sentiment and mental health. It was found that the participants have articulated conceptualizations regarding…

The Constitutive Power of Twitter

DEFF Research Database (Denmark)

Albu, Oana Brindusa; Etter, Michael Andreas

Twitter is an increasingly used new information and communication technology (ICT) in organizational settings. Predominant research, however, tends to adopt functionalist standpoints and investigates new ICTs as platforms of information transmission through which organizations interact with their......Twitter is an increasingly used new information and communication technology (ICT) in organizational settings. Predominant research, however, tends to adopt functionalist standpoints and investigates new ICTs as platforms of information transmission through which organizations interact...... with their constituents. Such focus leaves little knowledge concerning the tensions new ICTs bring to organizational life. For a more nuanced understanding of the constitutive role of new ICTs in organizing, this paper unfolds a communication centered perspective and examines the strategic Twitter use in two...... organizations. The analysis illustrates how specific Twitter interactions, i.e., hashtags, become hypertexts—a type of authoritative texts—which simultaneously constitute an organizational actor or act as a pastiche of it. The study contributes to extant research by illustrating how hypertextuality...

Academic information on Twitter: A user survey.

Directory of Open Access Journals (Sweden)

Ehsan Mohammadi

Full Text Available Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45% did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

Academic information on Twitter: A user survey.

Science.gov (United States)

Mohammadi, Ehsan; Thelwall, Mike; Kwasny, Mary; Holmes, Kristi L

2018-01-01

Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

Preservice Teachers' Microblogging: Professional Development via Twitter

Science.gov (United States)

Carpenter, Jeffrey

2015-01-01

Twitter has demonstrated potential to facilitate learning at the university level, and K-12 educators' use of the microblogging service Twitter to facilitate professional development appears to be on the rise. Research on microblogging as a part of teacher education is, however, limited. This paper investigates the use of Twitter by preservice…

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

Science.gov (United States)

Jung, Hyesil; Park, Hyeoun-Ae; Song, Tae-Min

2017-07-24

Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91

Finding Street Gang Members on Twitter

OpenAIRE

Balasuriya, Lakshika; Wijeratne, Sanjaya; Doran, Derek; Sheth, Amit

2016-01-01

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. ...

"We definitely need an audience": experiences of Twitter, Twitter networks and tweet content in adults with severe communication disabilities who use augmentative and alternative communication (AAC).

Science.gov (United States)

Hemsley, Bronwyn; Dann, Stephen; Palmer, Stuart; Allan, Meredith; Balandin, Susan

2015-01-01

The aim of this study was to investigate the Twitter experiences of adults with severe communication disabilities who use augmentative and alternative communication (AAC) to inform Twitter training and further research on the use of Twitter in populations with communication disabilities. This mixed methods research included five adults with severe communication disabilities who use AAC. It combined (a) quantitative analysis of Twitter networks and (b) manual coding of tweets with (c) narrative interviews with participants on their Twitter experiences and results. The five participants who used AAC and Twitter were diverse in their patterns and experiences of using Twitter. Twitter networks reflected interaction with a close-knit network of people rather than with the broader publics on Twitter. Conversational, Broadcast and Pass Along tweets featured most prominently, with limited use of News or Social Presence tweets. Tweets appeared mostly within each participant's micro- or meso-structural layers of Twitter. People who use AAC report positive experiences in using Twitter. Obtaining help in Twitter, and engaging in hashtag communities facilitated higher frequency of tweets and establishment of Twitter networks. Results reflected an inter-connection of participant Twitter networks that might form part of a larger as yet unexplored emergent community of people who use AAC in Twitter.

When a new technological product launching fails: A multi-method approach of facial recognition and E-WOM sentiment analysis.

Science.gov (United States)

Hernández-Fernández, Dra Asunción; Mora, Elísabet; Vizcaíno Hernández, María Isabel

2018-04-17

The dual aim of this research is, firstly, to analyze the physiological and unconscious emotional response of consumers to a new technological product and, secondly, link this emotional response to consumer conscious verbal reports of positive and negative product perceptions. In order to do this, biometrics and self-reported measures of emotional response are combined. On the one hand, a neuromarketing experiment based on the facial recognition of emotions of 10 subjects, when physical attributes and economic information of a technological product are exposed, shows the prevalence of the ambivalent emotion of surprise. On the other hand, a nethnographic qualitative approach of sentiment analysis of 67-user online comments characterise the valence of this emotion as mainly negative in the case and context studied. Theoretical, practical and methodological contributions are anticipated from this paper. From a theoretical point of view this proposal contributes valuable information to the product design process, to an effective development of the marketing mix variables of price and promotion, and to a successful selection of the target market. From a practical point of view, the approach employed in the case study on the product Google Glass provides empirical evidence useful in the decision making process for this and other technological enterprises launching a new product. And from a methodological point of view, the usefulness of integrated neuromarketing-eWOM analysis could contribute to the proliferation of this tandem in marketing research. Copyright © 2018 Elsevier Inc. All rights reserved.

Balancing Machine Work, Comfort Work, and Sentimental Work

DEFF Research Database (Denmark)

Pedersen, Maria Ie; Hansen, Magnus; Hertzum, Morten

2011-01-01

and attention. We investigate ambulance care in three of Denmark’s five healthcare regions, which staff ambulances with emergency medical technicians, paramedics, and physicians. Using the concept of illness trajectory we analyse how the ambulance crews balance machine work, which involves continuously...... monitoring the equipment, comfort work, which is actions taken to relieve the pain or discomfort of the patient, and sentimental work, which is care for the patient’s physical and mental well-being, often verbal in nature. The analysis shows that comfort and sentimental work often takes priority over machine...... work, but also that this has negative consequences. Equipment for use in ambulances should aim at supporting the ambulance crews in competently and dynamically balancing the different types of work and should, consequently, avoid binding the crew’s attention for unbroken periods of time....

Prediction of advertisement preference by fusing EEG response and sentiment analysis.

Science.gov (United States)

Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

2017-08-01

This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investor’s Sentiments and Stock Market Volatility: an empirical evidence from emerging stock market

Directory of Open Access Journals (Sweden)

Mobeen Ur Rehman

2013-05-01

Full Text Available The concept of efficient market hypothesis has prevailed the financial markets for a long time which says that the prices of the securities reflect all available information. This approach was mainly followed by the rational investors but with the passage of time, the concept of behavioral finance emerged due to some of the major global financial crashes. This concept states that there are investors trading in the market making decisions on the basis of sentiments not on any fundamental information. Such class of traders is called the noise traders and they are mainly responsible for any disruption in the returns of the securities. In this paper we will try to find whether these sentiments of the investors affect the returns of the securities listed on the Karachi stock exchange. We will use the investor sentiment index that uses the six proxies the data on which has been collected mainly from the Karachi stock exchange. Volatility of the stock market returns will be calculated and regressed with the sentimental equation discussed above as the independent variable. This study will help us to find out the extent to which these sentiments influence the stock market returns in weak form efficient market and also it will help us to identify the presence of such irrational noise traders in our financial market.

Twitter and Physics Professional Development

Science.gov (United States)

Nadji, Taoufik

2016-01-01

The advent of Twitter® and other social media services of its type ushered in a new era of professional development in education. This article addresses how a group of users have been employing Twitter to conduct professional development sessions that would benefit their participants by advancing their pedagogical approaches to learning and…

Conversation practices and network structure in Twitter

DEFF Research Database (Denmark)

Rossi, Luca; Magnani, Matteo

2012-01-01

The public by default nature of Twitter messages, together with the adoption of the #hashtag convention led, in few years, to the creation of a digital space able to host worldwide conversation on almost every kind of topic. From major TV shows to Natural disasters there is no contemporary event...... that does not have its own #hashtag to gather together the ongoing Twitter conversation. These topical discussions take place outside of the Twitter network made of followers and friends. Nevertheless this topical network is where many of the most studied phenomena take place. Therefore Twitter based...... communication exists on two almost autonomous levels: the Twitter network made of followers and friends that shows a certain level of stability and the topical network, characterized by a high level of contingency, that appears and disappears following the rhythm of a worldwide conversation. Despite the fact...

Twitter Use in the Hematopoietic Cell Transplantation Community.

Science.gov (United States)

Patel, Sagar S; Majhail, Navneet S

2018-02-01

Social media has revolutionized the access and exchange of information in healthcare. The microblogging platform Twitter has been used by blood and marrow transplant physicians over the last several years with increasing enthusiasm. We review the adoption of Twitter in the transplant community and its implications on clinical care, education, and research. Twitter allows instantaneous access to the latest research publications, developments at national and international meetings, networking with colleagues, participation in advocacy, and promoting available clinical trials. Additionally, Twitter serves as a gateway for resources dedicated to education and support for patients undergoing transplantation. We demonstrate the utilization and various applications in using Twitter among hematopoietic cell transplant healthcare professionals, patients, and other affiliated stakeholders. Professionalism concerns with clinician use of such social media platforms, however, also exist. Overall, Twitter has enhanced and increased the opportunities for engagement in the transplant community.

Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

Directory of Open Access Journals (Sweden)

Liliana Ibeth Barbosa-Santillán

2014-01-01

Full Text Available This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL. This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral {P,N,Z} depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

Secular tolerance? : Anti‐Muslim sentiment in Western Europe

NARCIS (Netherlands)

Ribberink, E.C.; Achterberg, P.H.J.; Houtman, D.

2017-01-01

he literature about secularization proposes two distinct explanations of anti-Muslim sentiment in secularized societies. The first theory understands it in terms of religious competition between Muslims and the remaining minority of orthodox Protestants; the second understands it as resulting from

#Stupidcancer: Exploring a Typology of Social Support and the Role of Emotional Expression in a Social Media Community.

Science.gov (United States)

Myrick, Jessica Gall; Holton, Avery E; Himelboim, Itai; Love, Brad

2016-01-01

Social network sites (SNSs) like Twitter continue to attract users, many of whom turn to these spaces for social support for serious illnesses like cancer. Building on literature that explored the functionality of online spaces for health-related social support, we propose a typology that situates this type of support in an SNS-based open cancer community based on the type (informational or emotional) and the direction (expression or reception) of support. A content analysis applied the typology to a 2-year span of Twitter messages using the popular hashtag "#stupidcancer." Given that emotions form the basis for much of human communication and behavior, including aspects of social support, this content analysis also examined the relationship between emotional expression and online social support in tweets about cancer. Furthermore, this study looked at the various ways in which Twitter allows for message sharing across a user's entire network (not just among the cancer community). This work thus begins to lay the conceptual and empirical groundwork for future research testing the effects of various types of social support in open, interactive online cancer communities.

Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data.

Science.gov (United States)

Schlichthorst, Marisa; King, Kylie; Turnure, Jackie; Sukunesan, Suku; Phelps, Andrea; Pirkis, Jane

2018-02-15

It has been suggested that some dominant aspects of traditional masculinity are contributing to the high suicide rates among Australian men. We developed a three-episode documentary called Man Up, which explores the complex relationship between masculinity and suicide and encourages men to question socially imposed rules about what it means to be a man and asks them to open up, express difficult emotions, and seek help if and when needed. We ran a three-phase social media campaign alongside the documentary using 5 channels (Twitter, Facebook, Instagram, YouTube, and Tumblr). This study aimed to examine the extent to which the Man Up Twitter campaign influenced the social media conversation about masculinity and suicide. We used Twitter insights data to assess the reach of and engagement with the campaign (using metrics on followers, likes, retweets, and impressions) and to determine the highest and lowest performing tweets in the campaign (using an aggregated performance measure of reactions). We used original content tweets to determine whether the campaign increased the volume of relevant Twitter conversations (aggregating the number of tweets for selected campaign hashtags over time), and we used a subset of these data to gain insight into the main content themes with respect to audience engagement. The campaign generated a strong following that was engaged with the content of the campaign; over its whole duration, the campaign earned approximately 5000 likes and 2500 retweets and gained around 1,022,000 impressions. The highest performing tweets posted by the host included video footage and occurred during the most active period of the campaign (around the screening of the documentary). The volume of conversations in relation to commonly used hashtags (#MANUP, #ABCMANUP, #LISTENUP, and #SPEAKUP) grew in direct relation to the campaign activities, achieving strongest growth during the 3 weeks when the documentary was aired. Strongest engagement was found with

Twitter, Millennials, and Nursing Education Research.

Science.gov (United States)

Stephens, Teresa M; Gunther, Mary E

2016-01-01

This article reports the use of Twitter as an intervention delivery method in a multisite experimental nursing research study. A form of social networking, Twitter is considered a useful means of communication, particularly with millennials. This method was chosen based on current literature exploring the characteristics of millennial students. Ahern's Model of Adolescent Resilience served as the theoretical framework. Participants were 70 junior-level baccalaureate nursing students, ages 19-23, at two state-supported universities. Twitter was found to be a convenient, cost-effective, and enjoyable means of intervention delivery for the researcher. Participants in the experimental and control groups expressed positive feelings about the use of Twitter. The findings contribute to future efforts to use social media in nursing research and education to increase faculty-student engagement, promote critical reflection, provide social support, reinforce course content, and increase the sense of community.

Predicting Learning-Related Emotions from Students' Textual Classroom Feedback via Twitter

Science.gov (United States)

Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz

2015-01-01

Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings textual feedback from students could provide…

Facebook vs. Twitter: Battle of the Social Network Stars

Science.gov (United States)

Tagtmeier, Curt

2010-01-01

Twitter. Facebook. These names stir up feelings, opinions, and experiences in just about everyone. As these services rise in popularity, libraries have begun to use them to reach out to patrons. Some libraries use Twitter but not Facebook. Some use Facebook but not Twitter. Some use both Facebook and Twitter, while others use neither. Yes, the…

The Twitter-thing (exhibition)

DEFF Research Database (Denmark)

Birkbak, Andreas; Bornakke, Tobias; Papazu, Irina Maria Clara Hansen

of multiple and constantly transforming issue-oriented publics? What kinds of issues come to the fore, how long does this last, and who associate themselves with them? The aim of the Twitter-thing is to trace the cuts issues make in a parliament. Each time a parliamentarian use a hashtag in a tweet, a link...... they are not necessarily aware of themselves as publics. At the same time, it is possible to self-select membership of these publics by using a specific hashtag. This raises the question of what feedback loops are at work between visualizations and those being visualized. How might a tool like the Twitter-thing change...... (parliamentary) politics? More generally, the tool prompts us to think about the fate of issues in institutionalized democracy. The Twitter-thing invites users to explore these questions by making the network available in an interactive format that makes it possible to zoom, search for particular politicians...

Who is more positive in private? : analyzing sentiment differences across privacy levels and demographic factors in Facebook chats and posts

NARCIS (Netherlands)

Gao, B.; Berendt, B.; Vanschoren, J.

2015-01-01

Understanding users' sentiments in social media is important in many domains, such as marketing and online applications. Is one demographic group inherently different from another? Does a group express the same sentiment both in private and public? How can we compare the sentiments of different

“We definitely need an audience”: experiences of Twitter, Twitter networks and tweet content in adults with severe communication disabilities who use augmentative and alternative communication (AAC)

Science.gov (United States)

Hemsley, Bronwyn; Dann, Stephen; Palmer, Stuart; Allan, Meredith; Balandin, Susan

2015-01-01

Abstract Purpose: The aim of this study was to investigate the Twitter experiences of adults with severe communication disabilities who use augmentative and alternative communication (AAC) to inform Twitter training and further research on the use of Twitter in populations with communication disabilities. Method: This mixed methods research included five adults with severe communication disabilities who use AAC. It combined (a) quantitative analysis of Twitter networks and (b) manual coding of tweets with (c) narrative interviews with participants on their Twitter experiences and results. Results: The five participants who used AAC and Twitter were diverse in their patterns and experiences of using Twitter. Twitter networks reflected interaction with a close-knit network of people rather than with the broader publics on Twitter. Conversational, Broadcast and Pass Along tweets featured most prominently, with limited use of News or Social Presence tweets. Tweets appeared mostly within each participant's micro- or meso-structural layers of Twitter. Conclusions: People who use AAC report positive experiences in using Twitter. Obtaining help in Twitter, and engaging in hashtag communities facilitated higher frequency of tweets and establishment of Twitter networks. Results reflected an inter-connection of participant Twitter networks that might form part of a larger as yet unexplored emergent community of people who use AAC in Twitter.Implications for RehabilitationTwitter can be used as an important vehicle for conversation and a forum for people with communication disabilities to exchange information and participate socially in online communities.It is important that information and resources relating to the effective use of Twitter for a range of purposes are made available to people with communication disabilities who wish to take up or maintain use of Twitter.People with communication disabilities might benefit from support in using Twitter to meet their goals

Twittering About Research: A Case Study of the World's First Twitter Poster Competition.

Science.gov (United States)

Randviir, Edward P; Illingworth, Samuel M; Baker, Matthew J; Cude, Matthew; Banks, Craig E

2015-01-01

The Royal Society of Chemistry held, to our knowledge, the world's first Twitter conference at 9am on February 5 (th), 2015. The conference was a Twitter-only conference, allowing researchers to upload academic posters as tweets, replacing a physical meeting. This paper reports the details of the event and discusses the outcomes, such as the potential for the use of social media to enhance scientific communication at conferences. In particular, the present work argues that social media outlets such as Twitter broaden audiences, speed up communication, and force clearer and more concise descriptions of a researcher's work. The benefits of poster presentations are also discussed in terms of potential knowledge exchange and networking. This paper serves as a proof-of-concept approach for improving both the public opinion of the poster, and the enhancement of the poster through an innovative online format that some may feel more comfortable with, compared to face-to-face communication.

Twitter as a Potential Disaster Risk Reduction Tool. Part II: Descriptive Analysis of Identified Twitter Activity during the 2013 Hattiesburg F4 Tornado.

Science.gov (United States)

Cooper, Guy Paul; Yeager, Violet; Burkle, Frederick M; Subbarao, Italo

2015-06-29

This article describes a novel triangulation methodological approach for identifying twitter activity of regional active twitter users during the 2013 Hattiesburg EF-4 Tornado. A data extraction and geographically centered filtration approach was utilized to generate Twitter data for 48 hrs pre- and post-Tornado. The data was further validated using six sigma approach utilizing GPS data. The regional analysis revealed a total of 81,441 tweets, 10,646 Twitter users, 27,309 retweets and 2637 tweets with GPS coordinates. Twitter tweet activity increased 5 fold during the response to the Hattiesburg Tornado.  Retweeting activity increased 2.2 fold. Tweets with a hashtag increased 1.4 fold. Twitter was an effective disaster risk reduction tool for the Hattiesburg EF-4 Tornado 2013.

Professional Twitter Development with Examples in NET 35

CERN Document Server

Crenna, Daniel

2009-01-01

Twitter is rapidly moving up the social networking food chain and is currently outranked by only Facebook and MySpace. It features a programming API that allows you to build Web sites and applications (both desktop and mobile) for reading and posting to Twitter, finding other Twitter users, aggregating Twitter content, and other uses. This book walks you through the process of combining many programming tools in order to build exciting, useful, and profitable applications. You'll begin with a look at RESTful services and examine how to structure your queries, handle asynchronous operations,

Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter

Science.gov (United States)

Amalia, Rizkiana; Arif Bijaksana, Moch; Darmantoro, Dhinta

2018-03-01

The presence of the word negation is able to change the polarity of the text if it is not handled properly it will affect the performance of the sentiment classification. Negation words in Indonesian are ‘tidak’, ‘bukan’, ‘belum’ and ‘jangan’. Also, there is a conjunction word that able to reverse the actual values, as the word ‘tetapi’, or ‘tapi’. Unigram has shortcomings in dealing with the existence of negation because it treats negation word and the negated words as separate words. A general approach for negation handling in English text gives the tag ‘NEG_’ for following words after negation until the first punctuation. But this may gives the tag to un-negated, and this approach does not handle negation and conjunction in one sentences. The rule-based method to determine what words negated by adapting the rules of Indonesian language syntactic of negation to determine the scope of negation was proposed in this study. With adapting syntactic rules and tagging “NEG_” using SVM classifier with RBF kernel has better performance results than the other experiments. Considering the average F1-score value, the performance of this proposed method can be improved against baseline equal to 1.79% (baseline without negation handling) and 5% (baseline with existing negation handling) for a dataset that all tweets contain negation words. And also for the second dataset that has the various number of negation words in document tweet. It can be improved against baseline at 2.69% (without negation handling) and 3.17% (with existing negation handling).

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

Science.gov (United States)

Guo, Kun; Sun, Yi; Qian, Xin

2017-03-01

With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.

Tweetin’ in the Rain: Exploring Societal-scale Effects of Weather on Mood

DEFF Research Database (Denmark)

Hannak, Aniko; Jørgensen, Sune Lehmann; Anderson, Eric

2012-01-01

There has been significant recent interest in using the aggregate sentiment from social media sites to understand and predict real-world phenomena. However, the data from social media sites also offers a unique and—so far—unexplored opportunity to study the impact of external factors on aggregate...... sentiment, at the scale of a society. Using a Twitterspecific sentiment extraction methodology, we the explore patterns of sentiment present in a corpus of over 1.5 billion tweets. We focus primarily on the effect of the weather and time on aggregate sentiment, evaluating how clearly the wellknown...... individual patterns translate into population-wide patterns. Using machine learning techniques on the Twitter corpus correlated with the weather at the time and location of the tweets, we find that aggregate sentiment follows distinct climate, temporal, and seasonal patterns....

Emotion Analysis on Social Big Data

Institute of Scientific and Technical Information of China (English)

REN Fuji; Kazuyuki Matsumoto

2017-01-01

In this paper, we describe a method of emotion analysis on social big data. Social big data means text data that is emerging on In-ternet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis. Because these data are constructed by manual annotation, their quality is high but their quantity is low. If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data, we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications, which we could not know before. In this paper, we create an emotion analysis model that integrate the high-quality emotion corpus and the automatic-constructed corpus that we created in our past studies, and then analyze a large-scale corpus consisting of Twitter tweets based on the model. As the result of time-series analysis on the large-scale corpus and the result of model evaluation, we show the effective-ness of our proposed method.

Seeing the elephant: Parsimony, functionalism, and the emergent design of contempt and other sentiments.

Science.gov (United States)

Gervais, Matthew M; Fessler, Daniel M T

2017-01-01

The target article argues that contempt is a sentiment, and that sentiments are the deep structure of social affect. The 26 commentaries meet these claims with a range of exciting extensions and applications, as well as critiques. Most significantly, we reply that construction and emergence are necessary for, not incompatible with, evolved design, while parsimony requires explanatory adequacy and predictive accuracy, not mere simplicity.

Geotagged US Tweets as Predictors of County-Level Health Outcomes, 2015-2016.

Science.gov (United States)

Nguyen, Quynh C; McCullough, Matt; Meng, Hsien-Wen; Paul, Debjyoti; Li, Dapeng; Kath, Suraj; Loomis, Geoffrey; Nsoesie, Elaine O; Wen, Ming; Smith, Ken R; Li, Feifei

2017-11-01

To leverage geotagged Twitter data to create national indicators of the social environment, with small-area indicators of prevalent sentiment and social modeling of health behaviors, and to test associations with county-level health outcomes, while controlling for demographic characteristics. We used Twitter's streaming application programming interface to continuously collect a random 1% subset of publicly available geo-located tweets in the contiguous United States. We collected approximately 80 million geotagged tweets from 603 363 unique Twitter users in a 12-month period (April 2015-March 2016). Across 3135 US counties, Twitter indicators of happiness, food, and physical activity were associated with lower premature mortality, obesity, and physical inactivity. Alcohol-use tweets predicted higher alcohol-use-related mortality. Social media represents a new type of real-time data that may enable public health officials to examine movement of norms, sentiment, and behaviors that may portend emerging issues or outbreaks-thus providing a way to intervene to prevent adverse health events and measure the impact of health interventions.

Getting started with Twitter Flight

CERN Document Server

Hamshere, Tom

2013-01-01

Getting Started with Twitter Flight is written with the intention to educate the readers, helping them learn how to build modular powerful applications with Flight, Twitter's cutting-edge JavaScript framework.This book is for anyone with a foundation in JavaScript who wants to build web applications. Flight is quick and easy to learn, built on technologies you already understand such as the DOM, events, and jQuery.

The Relationship between Market Sentiment Index and Stock Rates of Return: a Panel Data Analysis

Directory of Open Access Journals (Sweden)

Claudia Emiko Yoshinaga

2012-04-01

Full Text Available This article analyzes the relationship between market sentiment and future stock rates of return. We used amethodology based on principal component analysis to create a sentiment index for the Brazilian market withdata from 1999 to 2008. The sample consisted of companies listed on BM&FBOVESPA which were groupedinto quintiles, each representing a portfolio, according to the magnitude of the following characteristics: marketvalue, total annualized risk and listing time on BM&FBOVESPA. Next, we calculated the average return of eachportfolio for every quarter. The data for the first and last quintiles were analyzed via two-factor ANOVA, usingsentiment index of the previous period (positive or negative as the main factor and each characteristic ascontrolling factors. Finally, the sentiment index was included in a panel data pricing model. The results indicatea significant and negative relationship between the market sentiment index and the future rates of return. Thesefindings suggest the existence of a reversion pattern in stock returns, meaning that after a positive sentimentperiod, the impact on subsequent stock returns is negative, and vice-versa.

"To wipe a manly tear": the aesthetics of emotion in Victorian narrative painting.

Science.gov (United States)

Fletcher, Pamela

2009-01-01

Over the course of the twentieth century, Victorian narrative painting became synonymous with sentimentality, melodrama, and the artificial evocation of emotion. This essay aims to complicate this familiar assessment by examining the role of emotional effect played in aesthetic evaluations of some of the most popular modern life genre paintings of the 1850s to 1870s. I argue that the critical discourse on Victorian narrative painting was marked by a persistent skepticism about the role of feeling in aesthetic response -- as excessively painful or obvious emotional impact marked the limit between artistic success and failure -- and I locate these concerns within the physical and social exhibition culture of the Royal Academy.

Mining twitter to understand the smoking cessation barriers.

Science.gov (United States)

Krittanawong, Chayakrit; Wang, Zhen

2017-10-26

Smoking cessation is challenging and lack of positive support is a known major barrier to quitting cigarettes. Previous studies have suggested that social influences might increase smokers' awareness of social norms for appropriate behavior, which might lead to smoking cessation. Although social media use is increasing among young adults in the United States, research on the relationship between social media use and smoking cessation is lacking. Twitter has provided a rich source of information for researchers, but no overview exists as to how the field uses Twitter in smoking cessation research. To the best of our knowledge, this study conducted a data mining analysis of Twitter to assess barriers to smoking cessation. In conclusion, Twitter is a cost-effective tool with the potential to disseminate information on the benefits of smoking cessation and updated research to the Twitter community on a global scale.

California Digital Library in Twitter-Land

Science.gov (United States)

Starr, Joan

2010-01-01

In October 2009, California Digital Library (CDL), where the author serves as manager of strategic and project planning, jumped into the world of social networking by joining Twitter. From Twitter, the CDL staff publish the content of their monthly newsletter, "CDLINFO News," and also additional content created by CDL programs and…

Twitter and Non-Elites: Interpreting Power Dynamics in the Life Story of the (#)BRCA Twitter Stream.

Science.gov (United States)

Vicari, Stefania

2017-09-01

In May 2013 and March 2015, actress Angelina Jolie wrote in the New York Times about her choice to undergo preventive surgery. In her two op-eds, she explained that - as a carrier of the BRCA1 gene mutation - preventive surgery was the best way to lower her heightened risk of developing breast and ovarian cancer. By applying a digital methods approach to BRCA-related tweets from 2013 and 2015, before, during, and after the exposure of Jolie's story, this study maps and interprets Twitter discursive dynamics at two time points of the BRCA Twitter stream. Findings show an evolution in curation and framing dynamics occurring between 2013 and 2015, with individual patient advocates replacing advocacy organizations as top curators of BRCA content and coming to prominence as providers of specialist illness narratives. These results suggest that between 2013 and 2015, Twitter went from functioning primarily as an organization-centered news reporting mechanism, to working as a crowdsourced specialist awareness system. This article advances a twofold contribution. First, it points at Twitter's fluid functionality for an issue public and suggests that by looking at the life story-rather than at a single time point-of an issue-based Twitter stream, we can track the evolution of power roles underlying discursive practices and better interpret the emergence of non-elite actors in the public arena. Second, the study provides evidence of the rise of activist cultures that rely on fluid, non-elite, collective, and individual social media engagement.

On the relationship between investor sentiment, VIX and trading volume.

Directory of Open Access Journals (Sweden)

Simon Man Shing So

2015-11-01

Full Text Available As noise traders affect stock market by trading, sentiment, as a signal of noise, may have relationships with trading volume. This paper explores the effect of sentiment on the stock market’s trading volume. Increase in Volatility Index (VIX can explain the percentage increase in trading volume, but only in high VIX period. Besides, higher level of VIX is likely to be associated with greater variability of trading volume. The noise traders add liquidity to the market and provide more chances for investors to time their trade as the volatility of liquidity increases. These two kinds of impact lower rational investors’ required return. The noise traders not only drive the price deviating from fundamental value, but also influence the liquidity dimensions.

Twitter archiving using Twapper Keeper: technical and policy challenges

OpenAIRE

Kelly, Brian; Hawksey, Martin; O'Brien, John; Guy, Marieke; Rowe, Matthew

2010-01-01

Twitter is widely used in a range of different contexts, ranging from informal social communications and marketing purposes through to supporting various professional activities in teaching and learning and research. The growth in Twitter use has led to a recognition of the need to ensure that Twitter posts ('tweets') can be accessed and reused by a variety of third party applications. This paper describes development work to the Twapper Keeper Twitter archiving service to support use of Twit...

COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines

NARCIS (Netherlands)

Schouten, Kim; Frasincar, Flavius; de Jong, F.M.G.

2017-01-01

This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the

Criminal thinking styles and emotional intelligence in Egyptian offenders.

Science.gov (United States)

Megreya, Ahmed M

2013-02-01

The Psychological Inventory of Criminal Thinking Styles (PICTS) has been applied extensively to the study of criminal behaviour and cognition. Increasingly growing evidence indicates that criminal thinking styles vary considerably among individuals, and these individual variations appear to be crucial for a full understanding of criminal behaviour. This study aimed to examine individual differences in criminal thinking as a function of emotional intelligence. A group of 56 Egyptian male prisoners completed the PICTS and Bar-On Emotional Quotient Inventory (EQ-i). The correlations between these assessments were examined using a series of Pearson correlations coefficients, with Bonferroni correction. General criminal thinking, reactive criminal thinking and five criminal thinking styles (mollification, cutoff, power orientation, cognitive indolence and discontinuity) negatively correlated with emotional intelligence. On the other hand, proactive criminal thinking and three criminal thinking styles (entitlement, superoptimism and sentimentality) did not associate with emotional intelligence. Emotional intelligence is an important correlate of individual differences in criminal thinking, especially its reactive aspects. Practical implications of this suggestion were discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Applying linguistic methods to understanding smoking-related conversations on Twitter.

Science.gov (United States)

Sanders-Jackson, Ashley; Brown, Cati G; Prochaska, Judith J

2015-03-01

Social media, such as Twitter, have become major channels of communication and commentary on popular culture, including conversations on our nation's leading addiction: tobacco. The current study examined Twitter conversations following two tobacco-related events in the media: (1) President Obama's doctor announcing that he had quit smoking and (2) the release of a photograph of Miley Cyrus (a former Disney child star) smoking a cigarette. With a focus on high-profile individuals whose actions can draw public attention, we aimed to characterise tobacco-related conversations as an example of tobacco-related public discourse and to present a novel methodology for studying social media. Tweets were collected 11-13 November 2011 (President Obama) and 1-3 August 2011 (Miley Cyrus) and analysed for relative frequency of terms, a novel application of a linguistic methodology. The President Obama data set (N=2749 tweets) had conversations about him quitting tobacco as well as a preponderance of information on political activity, links to websites, racialised terms and mention of marijuana. Websites and terms about Obama's smoke-free status were most central to the conversation. In the Miley Cyrus data (N=4746 tweets), terms that occurred with the greatest relative frequency were positive, emotional and supportive of quitting (eg, love, and please), with words such as 'love' most central to the conversation. People are talking about tobacco-related issues on Twitter, and semantic network analysis can be used to characterise on-line conversations. Future interventions may be able to harness social media and major current events to raise awareness of smoking-related issues. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Emergency management, Twitter and social media evangelism

DEFF Research Database (Denmark)

Latonero, Mark; Shklovski, Irina

2011-01-01

media tools from the emergency management professional’s viewpoint with a particular focus on the use of Twitter. Limited research has investigated Twitter usage in crisis situations from an organizational perspective. This paper contributes to the understanding of organizational innovation, risk...... organizations face engaging with social media and Twitter. This article provides insights into practices and challenges of new media implementation for crisis and risk management organizations....

Evaluating college students’ displayed alcohol references on Facebook and Twitter

Science.gov (United States)

Moreno, Megan A.; Arseniev-Koehler, Alina; Litt, Dana; Christakis, Dimitri

2018-01-01

Purpose Current trends suggest adolescents and young adults typically maintain a social media “portfolio” of several sites including Facebook and Twitter, but little is known regarding how an individual chooses to display risk behaviors across these different sites. The purpose of this study was to investigate college students’ displayed alcohol references on both Facebook and Twitter. Methods Among a larger sample of college students from two universities, we identified participants who maintained both Facebook and Twitter profiles. Data collection included evaluation of 5 months of participants’ Facebook and Twitter posts for alcohol references, number of social connections (i.e. friends or followers) and number of posts. Phone interviews assessed participants’ frequency of Facebook and Twitter use and self-reported alcohol use. Analyses included Fisher’s exact test, Wilcoxon matched pair sign test, Freidman rank-sum tests and logistic regression. Results Of 112 eligible participants, 94 (RR=84.8%) completed the study. Participants were more likely to display alcohol references on Facebook compared to Twitter (76% versus 34%, p=0.02). Participants reported more social connections on Facebook versus Twitter (average 801.2 friends versus 189.4 followers, pTwitter (94.6% versus 50%, pTwitter displayed alcohol references, but mediators differed in each model. Discussion College students were more likely to display alcohol references on Facebook compared to Twitter. Understanding these patterns and predictors may inform prevention and intervention efforts directed at particular social media sites. PMID:26995291

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

Science.gov (United States)

Wang, Wei; Wang, Hongwei

2015-10-01

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

Learning by Tweeting: Using Twitter as a Pedagogical Tool

Science.gov (United States)

Rinaldo, Shannon B.; Tapp, Suzanne; Laverie, Debra A.

2011-01-01

Marketing professionals use Twitter extensively for communicating with and monitoring customers, for observing competitors, and for analyzing chatter concerning brands, products, and company image. Can professors use Twitter to engage students in conversation about a marketing course? The authors argue that Twitter has many benefits for marketing…

Creative participation: collective sentiment in online co-creation communities

NARCIS (Netherlands)

Lee, H.H.M.; van Dolen, W.

2015-01-01

Co-creation communities allow companies to utilize consumers’ creative thinking in the innovation process. This paper seeks to understand the role of sentiment in user co-creation. The results suggest that management style can affect the success of co-creation communities. Specific employees’

Towards an Italian Lexicon for Polarity Classification (polarITA): a Comparative Analysis of Lexical Resources for Sentiment Analysis

OpenAIRE

Hernández Farías, Delia Irazú; Laganà, Irene; Patti, Viviana; Bosco, Cristina

2018-01-01

The paper describes a preliminary study for the development of a novel lexicon for Italian sentiment analysis, i.e. where words are associated with polarity values. Given the influence of sentiment lexica on the performance of sentiment analysis systems, a methodology based on the detection and classification of errors in existing lexical resources is proposed and an extrinsic evaluation of the impact of such errors is applied. The final aim is to build a novel resource from the filtering app...

The perception of ethnic diversity and anti-immigrant sentiments: a multilevel analysis of local communities in Belgium

NARCIS (Netherlands)

Hooghe, Marc; de Vroome, Thomas

2015-01-01

Most of the literature suggests a positive relationship between immigrant concentration and anti-immigrant sentiments. The main goal of this study is to investigate the impact of both perceived and actual size of migrant populations on anti-immigrant sentiments. A representative survey of

Evaluating the Impact of Cooperative Extension Outreach via Twitter

Science.gov (United States)

O'Neill, Barbara

2014-01-01

Twitter is increasingly being used by Extension educators as a teaching and program-marketing tool. It is not enough, however, to simply use Twitter to disseminate information. Steps must be taken to evaluate program impact with quantitative and qualitative data. This article described the following Twitter evaluation metrics: unique hashtags,…

Use of Twitter at a major national pharmacy conference.

Science.gov (United States)

Awad, Nadia I; Cocchio, Craig

2015-01-01

The results of a study of Twitter use by attendees of the 2013 ASHP Midyear Clinical Meeting (MCM) and other interested parties are presented. All messages posted on the social media platform Twitter under the official MCM "hashtag" (#ashpmidyear) during the five-day conference were archived and evaluated. Demographic data on authors of MCM-related tweets were collected by evaluating information provided by Twitter users in their public profiles. The archived messages were classified by content type. A total of 1539 messages originating from 400 unique U.S.- and foreign-based Twitter accounts were posted under the MCM hashtag, an average of 3.84 tweets per account. The estimated rate of conference-related Twitter use by MCM attendees was 1.7%. One third of Twitter users posting conference-related tweets were identified as pharmacists; 86 (21.5%) and 65 (16.25%) tweets originated from accounts held by pharmacy students and pharmaceutical industry representatives, respectively. The authors of MCM-related tweets represented a broad cross-section of pharmacy practice settings and specialties. About 39% of the evaluated Twitter postings were classified as social, with about 31% of postings pertaining to specific MCM educational sessions and nearly 25% classified as advertising. The majority of MCM-related tweets by onsite and remote Twitter users were social in nature or pertained to educational sessions held over the course of the conference. Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Brief report Effects of spinal cord injuries on the subjective component of emotions.

Science.gov (United States)

Cobos, Pilar; Sánchez, María; Pérez, Nieves; Vila, Jaime

2004-02-01

Responses to a structured interview by 19 patients with spinal cord injuries (SCI) (7 women and 12 men) concerning their past (pre-injury) and present emotions were analysed and compared with responses by 19 SCI-free controls matched for sex, age, and education. In addition, subjects assessed the valence and arousal of 10 pleasant, 10 neutral, and 10 unpleasant pictures selected from the International Affective Picture System. The results indicate that there is no decrease in emotional experience among individuals with SCI compared with those without. For all the emotional scales (joy, love, sentimentalism, positive emotions as a whole, fear, anger, sadness, and negative emotions as a whole) the SCI group always showed either no change or an increase; this increase was significantly higher in SCI than in control subjects for sadness. No differences were observed between the two groups in the subjective assessment of the pictures. The implications of the results for the James versus Cannon controversy on the theory of emotions are discussed.

Sentiment Analysis of Text Guided by Semantics and Structure

NARCIS (Netherlands)

A.C. Hogenboom (Alexander)

2015-01-01

textabstractAs moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many

Combining Formal Logic and Machine Learning for Sentiment Analysis

DEFF Research Database (Denmark)

Petersen, Niklas Christoffer; Villadsen, Jørgen

2014-01-01

This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning...

Evaluating College Students' Displayed Alcohol References on Facebook and Twitter.

Science.gov (United States)

Moreno, Megan A; Arseniev-Koehler, Alina; Litt, Dana; Christakis, Dimitri

2016-05-01

Current trends suggest that adolescents and young adults typically maintain a social media "portfolio" of several sites including Facebook and Twitter, but little is known regarding how an individual chooses to display risk behaviors across these different sites. The purpose of this study was to investigate college students' displayed alcohol references on both Facebook and Twitter. Among a larger sample of college students from two universities, we identified participants who maintained both Facebook and Twitter profiles. Data collection included evaluation of 5 months of participants' Facebook and Twitter posts for alcohol references, number of social connections (i.e., friends or followers), and number of posts. Phone interviews assessed participants' frequency of Facebook and Twitter use and self-reported alcohol use. Analyses included Fisher's exact test, Wilcoxon matched pair sign test, Friedman rank-sum tests, and logistic regression. Of 112 eligible participants, 94 completed the study. Participants were more likely to display alcohol references on Facebook compared with those on Twitter (76% vs. 34%, p = .02). Participants reported more social connections on Facebook versus Twitter (average 801.2 friends vs. 189.4 followers, p Twitter (94.6% vs. 50%, p Twitter displayed alcohol references, but mediators differed in each model. College students were more likely to display alcohol references on Facebook compared with those on Twitter. Understanding these patterns and predictors may inform prevention and intervention efforts directed at particular social media sites. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Toward Real-Time Infoveillance of Twitter Health Messages.

Science.gov (United States)

Colditz, Jason B; Chu, Kar-Hai; Emery, Sherry L; Larkin, Chandler R; James, A Everette; Welling, Joel; Primack, Brian A

2018-06-21

There is growing interest in conducting public health research using data from social media. In particular, Twitter "infoveillance" has demonstrated utility across health contexts. However, rigorous and reproducible methodologies for using Twitter data in public health are not yet well articulated, particularly those related to content analysis, which is a highly popular approach. In 2014, we gathered an interdisciplinary team of health science researchers, computer scientists, and methodologists to begin implementing an open-source framework for real-time infoveillance of Twitter health messages (RITHM). Through this process, we documented common challenges and novel solutions to inform future work in real-time Twitter data collection and subsequent human coding. The RITHM framework allows researchers and practitioners to use well-planned and reproducible processes in retrieving, storing, filtering, subsampling, and formatting data for health topics of interest. Further considerations for human coding of Twitter data include coder selection and training, data representation, codebook development and refinement, and monitoring coding accuracy and productivity. We illustrate methodological considerations through practical examples from formative work related to hookah tobacco smoking, and we reference essential methods literature related to understanding and using Twitter data. (Am J Public Health. Published online ahead of print June 21, 2018: e1-e6. doi:10.2105/AJPH.2018.304497).

AAHSL Twitter Use From 2007 to 2014: An Exploratory Analysis.

Science.gov (United States)

Stellrecht, Elizabeth; Hendrix, Dean

2016-01-01

Twitter is a popular social media platform used by organizations for communication and marketing purposes. Many libraries, including members of the Association of Academic Health Sciences Libraries (AAHSL), have Twitter accounts, but how do these libraries use Twitter to communicate with their constituents and are they using it effectively? This study is a large-scale observational study of Twitter use within AAHSL libraries and reflects on the usage patterns present in the context of social media best practices. This study also aims to expand upon best practices for implementing and maintaining a Twitter account in a health sciences library setting.

Suffering Daughters and Wives. Sentimental Themes in Finnish and Nordic Realism

Directory of Open Access Journals (Sweden)

Saija Isomaa

2010-10-01

Full Text Available This article examines sentimental themes and scenarios in Nordic nineteenthcentury literature, focusing on Finnish realism. The main claim of the article is that the treatment of the Woman Question in Nordic literature is thematically connected to French sentimentalism that depicted upper-class women caught in the conflict between personal freedom and familial duties. Typical scenarios were family barrier to marriage and love triangle, in which an unhappily married woman fell in love with another man. French sentimental social novels took a stance on the position of women. Similar themes and scenarios can be found in Nordic nineteenth-century novels and plays. The ‘daughter novel’ tradition from Fredrika Bremer’s The President’s Daughters (1834 to Minna Canth’s Hanna (1886 depict the sufferings of upper-class girls in patriarchal family and society. A Doll’s House (1879 by Henrik Ibsen centers on the theme of conflicting duties, depicting the moral awakening of a doll-like wife, and Papin rouva (1893, ‘The Wife of a Clergyman’ by Juhani Aho concentrates on the sufferings and moral considerations of the unhappily married Elli. The article suggests that the sentimentalist legacy informs the Nordic nineteenth-century literature and should be taken into account in the scholarship.

Protests against #delhigangrape on Twitter: Analyzing India’s Arab Spring

Directory of Open Access Journals (Sweden)

Saifuddin Ahmed

2013-11-01

Full Text Available This study offers a comprehensive approach towards analyzing and explaining the role of Twitter in shaping and facilitating social movements especially during protests. It presents automatic and manual analyses of the tweet themes, usage characteristics and major Twitter users during a public outcry against a gangrape incident in Delhi, the capital city of India. Our results identified Twitter as an important channel for the diffusion of ideas and news among a vast set of adopters in defiance of geographical boundaries. Results of the content analyses highlight the prominent use of social media resources in disseminating information on Twitter, and the remarkable role of Twitter users as citizen journalists during the days of the protest. Results of the social network analysis suggest that major role players on Twitter were the offline protest leaders.

Social media networking: Facebook and Twitter.

Science.gov (United States)

Schneider, Andrew; Jackson, Rem; Baum, Neil

2010-01-01

The new wave of marketing and practice promotion will include social media networking. This article will discuss Facebook and Twitter. After reading this article you, will have an understanding of these two important aspects of social media and how you might use Facebook and Twitter in your practice to enhance your communication with your existing patients and attract new patients.

Examining ISIS Support and Opposition Networks on Twitter

Science.gov (United States)

2016-01-01

Examining ISIS Support and Opposition Networks on Twitter Elizabeth Bodine-Baron, Todd C. Helmus, Madeline Magnuson, Zev Winkelman C O R P O R A T...Syria (ISIS), like no other terrorist organization before, has used Twitter and other social media channels to broadcast its message, inspire followers...and recruit new fighters. Though much less heralded, ISIS opponents have also taken to Twitter to cas- tigate the ISIS message. This report draws on

Is Twitter a forum for disseminating research to health policy makers?

Science.gov (United States)

Kapp, Julie M; Hensel, Brian; Schnoring, Kyle T

2015-12-01

Findings from scientific research largely remain inside the scientific community. Research scientists are being encouraged to use social media, and especially Twitter, for dissemination of evidence. The potential for Twitter to narrow the gap on evidence translated into policy presents new opportunities. We explored the innovative question of the feasibility of Twitter as a tool for the scientific community to disseminate to and engage with health policy makers for research impact. We created a list of federal "health policy makers." In December 2014, we identified members using several data sources, then collected and summarized their Twitter usage data. Nearly all health policy makers had Twitter accounts. Their communication volume varied broadly. Policy makers are more likely to push information via Twitter than engage with constituents, although usage varied broadly. Twitter has the potential to aid the scientific community in dissemination of health-related research to health policy makers, after understanding how to effectively (and selectively) use Twitter. Copyright © 2015 Elsevier Inc. All rights reserved.

CSR Communication Strategies for Twitter : Microblogging as a Tool for Public Relations

OpenAIRE

Etter, Michael; Plotkowiak, Thomas; Stanoevska-Slabeva, Katarina

2011-01-01

This study explores how companies use the social media tool Twitter for CSR communication. By analyzing CSR communication conducted by 30 most central corporate Twitter accounts, identified through social network analysis within a CSR-Twitter-network consisting of 19'855 Twitter members, we contribute to the understanding of Twitter's role for CSR communication and public relations. Manually conducted content analysis of totally 41‘864 corporate Twitter messages gives insights into different ...

Conversation practices and network structure in Twitter

DEFF Research Database (Denmark)

Rossi, Luca; Magnani, Matteo

2012-01-01

that this double nature of Twitter is widely recognized among scholars there is still little literature facing the relationships between these two networks. This paper presents the results of an empirical research aimed at discovering how the Twitter network is affected by what happens on the topical network. Does...... the participation in the same hashtag based conversation change the follower list of the participants? Is it possible to point out specific social behaviors that would produce a major gain of followers? Our conclusions are based on real data concerning the popular TV show Xfactor, that largely used Twitter...

Facebook and Twitter For Seniors For Dummies

CERN Document Server

Collier, Marsha

2010-01-01

A fun and easy social media guide for the over-55 set. People over 55 were the fastest-growing user group on Facebook in the first half of 2009, and they're flocking to Twitter at a faster rate than their under-20 grandchildren. From basic information about establishing an Internet connection to rediscovering old friends, sharing messages and photos, and keeping in touch instantly with Twitter, this book by online expert Marsha Collier helps seniors jump right into social media.: Seniors are recognizing the communication possibilities of Facebook and Twitter and are signing up in record number

Twelve tips for using Twitter as a learning tool in medical education.

Science.gov (United States)

Forgie, Sarah Edith; Duff, Jon P; Ross, Shelley

2013-01-01

Twitter is an online social networking service, accessible from any Internet-capable device. While other social networking sites are online confessionals or portfolios of personal current events, Twitter is designed and used as a vehicle to converse and share ideas. For this reason, we believe that Twitter may be the most likely candidate for integrating social networking with medical education. Using current research in medical education, motivation and the use of social media in higher education, we aim to show the ways Twitter may be used as a learning tool in medical education. A literature search of several databases, online sources and blogs was carried out examining the use of Twitter in higher education. We created 12 tips for using Twitter as a learning tool and organized them into: the mechanics of using Twitter, suggestions and evidence for incorporating Twitter into many medical education contexts, and promoting research into the use of Twitter in medical education. Twitter is a relatively new social medium, and its use in higher education is in its infancy. With further research and thoughtful application of media literacy, Twitter is likely to become a useful adjunct for more personalized teaching and learning in medical education.

Predicting iPhone Sales from iPhone Tweets

DEFF Research Database (Denmark)

Lassen, Niels Buus; Madsen, Rene; Vatrapu, Ravi

2014-01-01

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying as...... sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.......Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying......, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate...

Evaluation of Twitter Users Writings about Teachers in Turkey

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Yavuz, Mustafa

2014-01-01

As a social sharing network whose number of users worldwide continues to rapidly increase, Twitter has become an active network for individuals to share their thoughts and feelings at any given time. The purpose of this work, then, is to evaluate Twitter users of Turkey in terms of how they write about their teachers on Twitter. In order to…

PhishAri: Automatic Realtime Phishing Detection on Twitter

OpenAIRE

Aggarwal, Anupama; Rajadesingan, Ashwin; Kumaraguru, Ponnurangam

2013-01-01

With the advent of online social media, phishers have started using social networks like Twitter, Facebook, and Foursquare to spread phishing scams. Twitter is an immensely popular micro-blogging network where people post short messages of 140 characters called tweets. It has over 100 million active users who post about 200 million tweets everyday. Phishers have started using Twitter as a medium to spread phishing because of this vast information dissemination. Further, it is difficult to det...

Twitter Conversation Patterns Related to Research Papers

Science.gov (United States)

Nelhans, Gustaf; Lorentzen, David Gunnarsson

2016-01-01

Introduction: This paper deals with what academic texts and datasets are referred to and discussed on Twitter. We used document object identifiers as references to these items. Method: We streamed tweets from the Twitter application programming interface including the strings "dx" and "doi" while simultaneously streaming tweets…

Life Satisfaction and the Pursuit of Happiness on Twitter

Science.gov (United States)

Yang, Chao; Srinivasan, Padmini

2016-01-01

Life satisfaction refers to a somewhat stable cognitive assessment of one’s own life. Life satisfaction is an important component of subjective well being, the scientific term for happiness. The other component is affect: the balance between the presence of positive and negative emotions in daily life. While affect has been studied using social media datasets (particularly from Twitter), life satisfaction has received little to no attention. Here, we examine trends in posts about life satisfaction from a two-year sample of Twitter data. We apply a surveillance methodology to extract expressions of both satisfaction and dissatisfaction with life. A noteworthy result is that consistent with their definitions trends in life satisfaction posts are immune to external events (political, seasonal etc.) unlike affect trends reported by previous researchers. Comparing users we find differences between satisfied and dissatisfied users in several linguistic, psychosocial and other features. For example the latter post more tweets expressing anger, anxiety, depression, sadness and on death. We also study users who change their status over time from satisfied with life to dissatisfied or vice versa. Noteworthy is that the psychosocial tweet features of users who change from satisfied to dissatisfied are quite different from those who stay satisfied over time. Overall, the observations we make are consistent with intuition and consistent with observations in the social science research. This research contributes to the study of the subjective well being of individuals through social media. PMID:26982323

Life Satisfaction and the Pursuit of Happiness on Twitter.

Directory of Open Access Journals (Sweden)

Chao Yang

Full Text Available Life satisfaction refers to a somewhat stable cognitive assessment of one's own life. Life satisfaction is an important component of subjective well being, the scientific term for happiness. The other component is affect: the balance between the presence of positive and negative emotions in daily life. While affect has been studied using social media datasets (particularly from Twitter, life satisfaction has received little to no attention. Here, we examine trends in posts about life satisfaction from a two-year sample of Twitter data. We apply a surveillance methodology to extract expressions of both satisfaction and dissatisfaction with life. A noteworthy result is that consistent with their definitions trends in life satisfaction posts are immune to external events (political, seasonal etc. unlike affect trends reported by previous researchers. Comparing users we find differences between satisfied and dissatisfied users in several linguistic, psychosocial and other features. For example the latter post more tweets expressing anger, anxiety, depression, sadness and on death. We also study users who change their status over time from satisfied with life to dissatisfied or vice versa. Noteworthy is that the psychosocial tweet features of users who change from satisfied to dissatisfied are quite different from those who stay satisfied over time. Overall, the observations we make are consistent with intuition and consistent with observations in the social science research. This research contributes to the study of the subjective well being of individuals through social media.

Student Teachers’ Attitude towards Twitter for Educational Aims

Directory of Open Access Journals (Sweden)

Victoria I. Marín

2014-07-01

Full Text Available This paper presents an educational experience with 100 student teachers from different courses of the University of the Balearic Islands (Spain in which Twitter is used for various different activities. The aim of this experiment was to explore student teachers’ perceptions in order to value their attitude towards Twitter for educational aims. Afterwards, students were asked to write down their reflections on an eportfolio. Data was collected from their eportfolio evidence, which was analysed to review their attitude towards the use of Twitter for educational purposes and for their future teaching and professional development. The conclusions indicate the need to conduct different educational activities in which Twitter is used in various ways. In addition, conclusions reflect on the real impact of Twitter on students’ learning enhancement, in order to improve student teachers’ attitudes towards social media in education. Therefore, this article contributes to the body of existing research on the use of technology in education, specifically to the possibilities of the use of social media and microblogging in Teacher Education.

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

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Ranco, Gabriele; Bordino, Ilaria; Bormetti, Giacomo; Caldarelli, Guido; Lillo, Fabrizio; Treccani, Michele

2016-01-01

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

Directory of Open Access Journals (Sweden)

Gabriele Ranco

Full Text Available The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

Does twitter song amplitude signal male arousal in redwings (Turdus iliacus)?

DEFF Research Database (Denmark)

Lampe, H.M.; Balsby, T.J.S.; Espmark, Y.O.

2010-01-01

Bird songs may vary in amplitude for several reasons. Variations due to differences in environmental conditions are well known but whether signal information varies with song amplitude is less well known. In some species quiet songs are heard as a soft twitter. These twitter songs are common...... in Turdus species and may be used during escalated close range encounters when a quiet song will attract less attention from others. Male redwings (T. iliacus) sing a terminating twitter part that is quieter and highly variable both between and within males compared with the introductory motif part....... The twitter song of redwings, however, is often louder than the twitter in other Turdus species, especially during escalated song encounters. The seasonal variation in twitter duration also suggests that the twitter may signal increased aggression. We tested how male redwings responded to an assumed...

A Linguistic Analysis of Suicide-Related Twitter Posts.

Science.gov (United States)

O'Dea, Bridianne; Larsen, Mark E; Batterham, Philip J; Calear, Alison L; Christensen, Helen

2017-09-01

Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. This study aims to characterize the linguistic profiles of suicide-related Twitter posts. Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles. When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found. The predictive validity of the identified features needs further testing before these results can be used for interventional purposes. This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.

Effect of a Novel Engagement Strategy Using Twitter on Test Performance.

Science.gov (United States)

Webb, Amanda L; Dugan, Adam; Burchett, Woodrow; Barnett, Kelly; Patel, Nishi; Morehead, Scott; Silverberg, Mark; Doty, Christopher; Adkins, Brian; Falvo, Lauren

2015-11-01

Medical educators in recent years have been using social media for more penetrance to technologically-savvy learners. The utility of using Twitter for curriculum content delivery has not been studied. We sought to determine if participation in a social media-based educational supplement would improve student performance on a test of clinical images at the end of the semester. 116 second-year medical students were enrolled in a lecture-based clinical medicine course, in which images of common clinical exam findings were presented. An additional, optional assessment was performed on Twitter. Each week, a clinical presentation and physical exam image (not covered in course lectures) were distributed via Twitter, and students were invited to guess the exam finding or diagnosis. After the completion of the course, students were asked to participate in a slideshow "quiz" with 24 clinical images, half from lecture and half from Twitter. We conducted a one-way analysis of variance to determine the effect Twitter participation had on total, Twitter-only, and lecture-only scores. Twitter participation data was collected from the end-of-course survey and was defined as submitting answers to the Twitter-only questions "all or most of the time", "about half of the time", and "little or none of the time." We found a significant difference in overall scores (pTwitter-only scores (pTwitter "all or most of the time" or "about half the time" had significantly higher overall scores and Twitter-only scores (pTwitter than from traditional classroom lecture, some retention was noted. Future research on social media in medical education would benefit from clear control and experimental groups in settings where quantitative use of social media could be measured. Ultimately, it is unlikely for social media to replace lecture in medical curriculum; however, there is a reasonable role for social media as an adjunct to traditional medical education.

Leveraging Twitter to Maximize the Radiology Meeting Experience.

Science.gov (United States)

Kalia, Vivek; Ortiz, Daniel A; Patel, Amy K; Moriarity, Andrew K; Canon, Cheri L; Duszak, Richard

2018-01-01

Over recent years, social media engagement has proliferated among physicians, health care systems, scientific journals, professional societies, and patients. In radiology, Twitter (Twitter Inc, San Francisco, California) has rapidly become the preferred social media engagement tool and is now an essential activity at many large radiology society meetings. Twitter offers a versatile, albeit simple, platform for anyone interested in engaging with others, regardless of title, stature, or geography. In radiology and other medical specialties, year-after-year increases in Twitter engagement before, during, and after professional society meetings continue with widespread positive feedback. This short-form messaging tool also allows users to connect and interact with high-impact individuals and organizations on an ongoing basis (rather than once a year during large meetings). Through live-polling, Twitter also has the power to gather global opinions on issues highly relevant to radiology's future, such as the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) or breast cancer screening. Also increasingly popular is "live-tweeting" of curated meeting content, which makes information from the meeting accessible to a global audience. Despite the promise of growing professional networks and enabling discussions that cross geographic boundaries, the risks of Twitter use during radiology meetings must be recognized and mitigated. These include posting of unpublished data without consent (eg, slide content captured on camera phones), propagation of misinformation, and copyright infringement. Despite these issues and with an eye towards professionalism, Twitter can nonetheless be used effectively to increase engagement among radiologists, radiology societies, and patients. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Predicting Cyber Events by Leveraging Hacker Sentiment

OpenAIRE

Deb, Ashok; Lerman, Kristina; Ferrara, Emilio

2018-01-01

Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups' collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major ...

Using Twitter to Measure Public Discussion of Diseases: A Case Study

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Schwartz, H Andrew; Hill, Shawndra; Merchant, Raina M; Arango, Catalina; Ungar, Lyle

2015-01-01

Background Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language. Objective We characterized the extent of these biases and how they vary with disease. Methods We correlated self-reported prevalence rates for 22 diseases from Experian’s Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, “heart attack” on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease’s overrepresentation or underrepresentation on Twitter, relative to its prevalence. Results Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of .208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to .366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most

Can Big Data Machines Analyze Stock Market Sentiment?

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Dhar, Vasant

2014-12-01

Do the massive amounts of social and professionally curated data on the Internet contain useful sentiment about the market that "big data machines" can extract systematically? If so, what are the important challenges in creating economic value from these diffuse sources? In this commentary, I delve into these questions and frame the challenges involved using recent market developments as an illustrative backdrop.

Análisis cibermétrico y visual de Twitter

OpenAIRE

Zheng, Zhangxian; Alonso-Berrocal, José-Luis; G. Figuerola, Carlos

2013-01-01

This paper try to solve the necessity of collect the profile, followers and followed of a Twitter user via API and develop a crawler application use the library Python-Twitter, with the aim of make an analysis and visualization of the Twitter users network.

Toward a Critical-Sentimental Orientation in Human Rights Education

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Zembylas, Michalinos

2016-01-01

This paper addresses one of the challenges in human rights education (HRE) concerning the conceptualization of a pedagogical orientation that avoids both the pitfalls of a purely juridical address and a "cheap sentimental" approach. The paper uses as its point of departure Richard Rorty's key intervention on human rights discourse and…

Twitter as equipment for educational interaction

DEFF Research Database (Denmark)

Tække, Jesper

As a researcher in the action research project Socio Media Education (SME) I have wondered why it has been so difficult for the participants to integrate the, through successful actions, developed uses of Twitter in their everyday practice. In the research project, the test class and its teachers...... through actions and experimentation, in an exemplary manner have developed a number of applications of the interaction medium Twitter in the educational interaction. These uses seem clearly to facilitate student learning, increase their participation and commitment and reduce their Internet...... class’ everyday use of Twitter. This shows that the class does not generally enjoy the learning and attention catching developed teaching methods. Only when a teacher takes the initiative, which they do not do all, and nobody always do, the methods are used. Through a cross-optic established...

Modelling Political Disaffection from Twitter Data

DEFF Research Database (Denmark)

Monti, Corrado; Rozza, Alessandro; Zappella, Giovanni

2013-01-01

Twitter is one of the most popular micro-blogging services in the world, often studied in the context of political opinion mining for its peculiar nature of online public discussion platform. In our work we analyse the phenomenon of political disaffection defined as the “lack of confidence...... of this attitude. For this reason, we collect a massive database of Italian Twitter data (about 35 millions of tweets from April 2012 to October 2012) and we exploit scalable state-of-the-art machine learning techniques to generate time-series concerning the political disaffection discourse. In order to validate...... the quality of the time-series generated, we compare them with indicators of political disaffection from public opinion surveys. We find political disaffection on Twitter to be highly correlated with the indicators of political disaffection in the public opinion surveys. Moreover, we show the peaks...

Mining Twitter Data to Augment NASA GPM Validation

Science.gov (United States)

Teng, Bill; Albayrak, Arif; Huffman, George; Vollmer, Bruce; Loeser, Carlee; Acker, Jim

2017-01-01

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value

The emerging use of Twitter by urological journals.

Science.gov (United States)

Nason, Gregory J; O'Kelly, Fardod; Kelly, Michael E; Phelan, Nigel; Manecksha, Rustom P; Lawrentschuk, Nathan; Murphy, Declan G

2015-03-01

To assess the emerging use of Twitter by urological journals. A search of the Journal of Citation Reports 2012 was performed to identify urological journals. These journals were then searched on Twitter.com. Each journal website was accessed for links to social media (SoMe). The number of 'tweets', followers and age of profile was determined. To evaluate the content, over a 6-month period (November 2013 to April 2014), all tweets were scrutinised on the journals Twitter profiles. To assess SoMe influence, the Klout score of each journal was also calculated. In all, 33 urological journals were identified. Eight (24.2%) had Twitter profiles. The mean (range) number of tweets and followers was 557 (19-1809) and 1845 (82-3692), respectively. The mean (range) age of the twitter profiles was 952 (314-1758) days with an average 0.88 tweets/day. A Twitter profile was associated with a higher mean impact factor of the journal (mean [sd] 3.588 [3.05] vs 1.78 [0.99], P = 0.013). Over a 6-month period, November 2013 to April 2014, the median (range) number of tweets per profile was 82 (2-415) and the median (range) number of articles linked to tweets was 73 (0-336). Of these 710 articles, 152 were Level 1 evidence-based articles, 101 Level 2, 278 Level 3 and 179 Level 4. The median (range) Klout score was 47 (19-58). The Klout scores of major journals did not exactly mirror their impact factors. SoMe is increasingly becoming an adjunct to traditional teaching methods, due to its convenient and user-friendly platform. Recently, many of the leading urological journals have used Twitter to highlight significant articles of interest to readers. © 2014 The Authors. BJU International © 2014 BJU International.

’n (Outobiografiese Twitter-teologie

Directory of Open Access Journals (Sweden)

Jan-Albert van den Berg

2016-11-01

Full Text Available An (autobiographical Twitter-theology. Due to the increasing challenges created by an evolving digital world, traditional expressions of the Christian faith could become irrelevant for a fast-paced world. Through an autobiographical orientation, a search for meaningful personal expressions of the Christian faith on Twitter is traced and mapped down. Facilitated through a practical-theological inquiry and employing a qualitative empirical research methodology, personal aphorisms of the Christian faith on Twitter are traced down and presented as possible examples of a relevant digital autobiographical theology. Through the contribution of these empirical realities, new hermeneutical outcomes and a strategic involvement are facilitated. The creation, development and meaning of new theological formulations and articulations are explored and described through these expressions. In the tracing of and in the mapping down of these new expressions of faith, demarcations of a possible lived spirituality in the digital sphere are sounded out and verbalised. Through the documentation of these new and relevant articulations of the language of faith, a contribution is made to a meaningful digital autobiographical theology.

Does Twitter trigger bursts in signature collections?

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Yamaguchi, Rui; Imoto, Seiya; Kami, Masahiro; Watanabe, Kenji; Miyano, Satoru; Yuji, Koichiro

2013-01-01

The quantification of social media impacts on societal and political events is a difficult undertaking. The Japanese Society of Oriental Medicine started a signature-collecting campaign to oppose a medical policy of the Government Revitalization Unit to exclude a traditional Japanese medicine, "Kampo," from the public insurance system. The signature count showed a series of aberrant bursts from November 26 to 29, 2009. In the same interval, the number of messages on Twitter including the keywords "Signature" and "Kampo," increased abruptly. Moreover, the number of messages on an Internet forum that discussed the policy and called for signatures showed a train of spikes. In order to estimate the contributions of social media, we developed a statistical model with state-space modeling framework that distinguishes the contributions of multiple social media in time-series of collected public opinions. We applied the model to the time-series of signature counts of the campaign and quantified contributions of two social media, i.e., Twitter and an Internet forum, by the estimation. We found that a considerable portion (78%) of the signatures was affected from either of the social media throughout the campaign and the Twitter effect (26%) was smaller than the Forum effect (52%) in total, although Twitter probably triggered the initial two bursts of signatures. Comparisons of the estimated profiles of the both effects suggested distinctions between the social media in terms of sustainable impact of messages or tweets. Twitter shows messages on various topics on a time-line; newer messages push out older ones. Twitter may diminish the impact of messages that are tweeted intermittently. The quantification of social media impacts is beneficial to better understand people's tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore information hidden in social phenomena.

Does Twitter trigger bursts in signature collections?

Directory of Open Access Journals (Sweden)

Rui Yamaguchi

Full Text Available INTRODUCTION: The quantification of social media impacts on societal and political events is a difficult undertaking. The Japanese Society of Oriental Medicine started a signature-collecting campaign to oppose a medical policy of the Government Revitalization Unit to exclude a traditional Japanese medicine, "Kampo," from the public insurance system. The signature count showed a series of aberrant bursts from November 26 to 29, 2009. In the same interval, the number of messages on Twitter including the keywords "Signature" and "Kampo," increased abruptly. Moreover, the number of messages on an Internet forum that discussed the policy and called for signatures showed a train of spikes. METHODS AND FINDINGS: In order to estimate the contributions of social media, we developed a statistical model with state-space modeling framework that distinguishes the contributions of multiple social media in time-series of collected public opinions. We applied the model to the time-series of signature counts of the campaign and quantified contributions of two social media, i.e., Twitter and an Internet forum, by the estimation. We found that a considerable portion (78% of the signatures was affected from either of the social media throughout the campaign and the Twitter effect (26% was smaller than the Forum effect (52% in total, although Twitter probably triggered the initial two bursts of signatures. Comparisons of the estimated profiles of the both effects suggested distinctions between the social media in terms of sustainable impact of messages or tweets. Twitter shows messages on various topics on a time-line; newer messages push out older ones. Twitter may diminish the impact of messages that are tweeted intermittently. CONCLUSIONS: The quantification of social media impacts is beneficial to better understand people's tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore

Public comment sentiment on educational videos: Understanding the effects of presenter gender, video format, threading, and moderation on YouTube TED talk comments.

Directory of Open Access Journals (Sweden)

George Veletsianos

Full Text Available Scholars, educators, and students are increasingly encouraged to participate in online spaces. While the current literature highlights the potential positive outcomes of such participation, little research exists on the sentiment that these individuals may face online and on the factors that may lead some people to face different types of sentiment than others. To investigate these issues, we examined the strength of positive and negative sentiment expressed in response to TEDx and TED-Ed talks posted on YouTube (n = 655, the effect of several variables on comment and reply sentiment (n = 774,939, and the projected effects that sentiment-based moderation would have had on posted content. We found that most comments and replies were neutral in nature and some topics were more likely than others to elicit positive or negative sentiment. Videos of male presenters showed greater neutrality, while videos of female presenters saw significantly greater positive and negative polarity in replies. Animations neutralized both the negativity and positivity of replies at a very high rate. Gender and video format influenced the sentiment of replies and not just the initial comments that were directed toward the video. Finally, we found that using sentiment as a way to moderate offensive content would have a significant effect on non-offensive content. These findings have far-reaching implications for social media platforms and for those who encourage or prepare students and scholars to participate online.

Public comment sentiment on educational videos: Understanding the effects of presenter gender, video format, threading, and moderation on YouTube TED talk comments

Science.gov (United States)

Kimmons, Royce; Larsen, Ross; Dousay, Tonia A.; Lowenthal, Patrick R.

2018-01-01

Scholars, educators, and students are increasingly encouraged to participate in online spaces. While the current literature highlights the potential positive outcomes of such participation, little research exists on the sentiment that these individuals may face online and on the factors that may lead some people to face different types of sentiment than others. To investigate these issues, we examined the strength of positive and negative sentiment expressed in response to TEDx and TED-Ed talks posted on YouTube (n = 655), the effect of several variables on comment and reply sentiment (n = 774,939), and the projected effects that sentiment-based moderation would have had on posted content. We found that most comments and replies were neutral in nature and some topics were more likely than others to elicit positive or negative sentiment. Videos of male presenters showed greater neutrality, while videos of female presenters saw significantly greater positive and negative polarity in replies. Animations neutralized both the negativity and positivity of replies at a very high rate. Gender and video format influenced the sentiment of replies and not just the initial comments that were directed toward the video. Finally, we found that using sentiment as a way to moderate offensive content would have a significant effect on non-offensive content. These findings have far-reaching implications for social media platforms and for those who encourage or prepare students and scholars to participate online. PMID:29856749

Public comment sentiment on educational videos: Understanding the effects of presenter gender, video format, threading, and moderation on YouTube TED talk comments.

Science.gov (United States)

Veletsianos, George; Kimmons, Royce; Larsen, Ross; Dousay, Tonia A; Lowenthal, Patrick R

2018-01-01

Scholars, educators, and students are increasingly encouraged to participate in online spaces. While the current literature highlights the potential positive outcomes of such participation, little research exists on the sentiment that these individuals may face online and on the factors that may lead some people to face different types of sentiment than others. To investigate these issues, we examined the strength of positive and negative sentiment expressed in response to TEDx and TED-Ed talks posted on YouTube (n = 655), the effect of several variables on comment and reply sentiment (n = 774,939), and the projected effects that sentiment-based moderation would have had on posted content. We found that most comments and replies were neutral in nature and some topics were more likely than others to elicit positive or negative sentiment. Videos of male presenters showed greater neutrality, while videos of female presenters saw significantly greater positive and negative polarity in replies. Animations neutralized both the negativity and positivity of replies at a very high rate. Gender and video format influenced the sentiment of replies and not just the initial comments that were directed toward the video. Finally, we found that using sentiment as a way to moderate offensive content would have a significant effect on non-offensive content. These findings have far-reaching implications for social media platforms and for those who encourage or prepare students and scholars to participate online.

[Using Twitter in oncology. Research, continuing education, and advocacy].

Science.gov (United States)

De Fiore, Luciano; Ascierto, Paolo

2015-01-01

Traditional mass media coverage has been enhanced by Twitter, an interactive, real-time media, useful in health care, and particularly in oncology. Social media such as Twitter are gaining increasing acceptance as tools for instantaneous scientific dialogue. Professional medical societies such as ASCO and ESMO are using microblogging to expand the reach of scientific communications at and around their scientific meetings. To widen the message and maximize the potential for word-of-mouth marketing using Twitter, organizations (such as AIOM, ASCO or ESMO) and industries need a strategic communications plan to ensure on-going social media conversations. Twitter is a very powerful tool indeed that amplifies the results of scientific meetings, and conference organisers should put in place strategies to capitalise on this. This review demonstrates that cancer patients also share information more and more via Twitter about their disease, including diagnosis, symptoms, and treatments. This information could prove useful to health care providers.

The Impact of an AirBnb Host's Listing Description 'Sentiment' and Length On Occupancy Rates

OpenAIRE

Martinez, Richard Diehl; Carrington, Anthony; Kuo, Tiffany; Tarhuni, Lena; Abdel-Motaal, Nour Adel Zaki

2017-01-01

There has been significant literature regarding the way product review sentiment affects brand loyalty. Intrigued by how natural language influences consumer choice, we were motivated to examine whether an AirBnb host's occupancy rate (how often their listing is booked out of the days they indicated their listing was available) can be determined by the perceived sentiment and length of their description summary. Our main goal, more generally, was to determine which features, including (but no...

A Systematic Identification of Scientists on Twitter

Energy Technology Data Exchange (ETDEWEB)

Ke, Q.; Ahn, Y.Y.; Sugimoto, C.R.

2016-07-01

There is an increasing use of Twitter and other social media to estimate the broader social impacts of scholarship. However, without systematic understanding of the entities that participate in conversations about science, efforts to translate altmetrics into impact indicators may produce highly misleading results. Here we present a systematic approach to identifying scientists on Twitter. (Author)

Public sentiment and discourse about Zika virus on Instagram.

Science.gov (United States)

Seltzer, E K; Horst-Martz, E; Lu, M; Merchant, R M

2017-09-01

Social media have strongly influenced the awareness and perceptions of public health emergencies, and a considerable amount of social media content is now shared through images, rather than text alone. This content can impact preparedness and response due to the popularity and real-time nature of social media platforms. We sought to explore how the image-sharing platform Instagram is used for information dissemination and conversation during the current Zika outbreak. This was a retrospective review of publicly posted images about Zika on Instagram. Using the keyword '#zika' we identified 500 images posted on Instagram from May to August 2016. Images were coded by three reviewers and contextual information was collected for each image about sentiment, image type, content, audience, geography, reliability, and engagement. Of 500 images tagged with #zika, 342 (68%) contained content actually related to Zika. Of the 342 Zika-specific images, 299 were coded as 'health' and 193 were coded 'public interest'. Some images had multiple 'health' and 'public interest' codes. Health images tagged with #zika were primarily related to transmission (43%, 129/299) and prevention (48%, 145/299). Transmission-related posts were more often mosquito-human transmission (73%, 94/129) than human-human transmission (27%, 35/129). Mosquito bite prevention posts outnumbered safe sex prevention; (84%, 122/145) and (16%, 23/145) respectively. Images with a target audience were primarily aimed at women (95%, 36/38). Many posts (60%, 61/101) included misleading, incomplete, or unclear information about the virus. Additionally, many images expressed fear and negative sentiment, (79/156, 51%). Instagram can be used to characterize public sentiment and highlight areas of focus for public health, such as correcting misleading or incomplete information or expanding messages to reach diverse audiences. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights

Representasi Kepemimpinan Calon Presiden di Twitter

Directory of Open Access Journals (Sweden)

Nurul Hasfi

2017-05-01

Full Text Available Twitter is a new political communication channel massively used during Indonesian presidential election 2014. The character of the medium identified as interactive, participative and decentralized become main reasons why experts optimist on how this new media develop democratization. However, based on political situation in the context of Indonesian presidential election 2014, the argument needs to be confirmed. Twitter with its democratizing character, on the other hand has contradiction impact in facilitating irrational discussion, particularly run under mechanism of imagology politic. It is found in this research that implements Critical Discourse Analysis and confirmed with deliberative democracy theory. The study concludes that successful political discussion on Twitter about political leadership of presidential candidate was not determined by rational discussion (egalitarian, consensus, equal communication, etc but it’s merely determined by how skillful political actor construct the image of political leadership of the presidential candidate.

Let's Have a Tweetup: The Case for Using Twitter Professionally.

Science.gov (United States)

Fuller, Maren Y; Allen, Timothy Craig

2016-09-01

Social media use is very common and can be an effective way for professionals to discuss information and interact with colleagues. Twitter (Twitter, Inc, San Francisco, California) is a social media network where posts, termed tweets, are limited to 140 characters. Professional use of Twitter is ideal for physicians interested in both networking and education and is optimally used to facilitate in-person networking. Live-tweeting (posting real-time reactions to events) at professional meetings is also a popular and highly successful use of Twitter. Physicians report patient privacy as the top concern preventing use of social media for professional reasons, and although generally social media use is safe, it is essential to understand how to protect patient confidentially. Other social media platforms with potential for professional use include Facebook (Facebook, Inc, Menlo Park, California), Instagram (Facebook, Inc), YouTube (YouTube, LLC, San Bruno, California), and Periscope (Twitter, Inc). With Twitter and other social media options, now is the time for pathologists to increase our visibility on social media and worldwide.

Astropixie: Astronomy Engagement Through Blogging and Twitter

Science.gov (United States)

Bauer, A. E.

2013-04-01

I discuss the astronomy outreach and public engagement potential of blogging, based on experience writing and maintaining my astropixie blog since 2006 and maintaining a twitter account as @astropixie since 2008. These methods of social media allow for direct engagement with a public audience, increase public science literacy, provide understandable information beyond what can be presented in the media, diversify the image of scientists, publicize and provide feedback on current research, develop a community among readers, and inspire students. I also briefly discuss some professional benefits of using the social media resource of twitter. The goal of this paper is to give an idea of what blogs and twitter can provide as outreach tools, and to provide basic information about using these media.

On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure.

Science.gov (United States)

Volkova, Svitlana; Bachrach, Yoram

2015-12-01

Social media services such as Twitter and Facebook are virtual environments where people express their thoughts, emotions, and opinions and where they reveal themselves to their peers. We analyze a sample of 123,000 Twitter users and 25 million of their tweets to investigate the relation between the opinions and emotions that users express and their predicted psychodemographic traits. We show that the emotions that we express on online social networks reveal deep insights about ourselves. Our methodology is based on building machine learning models for inferring coarse-grained emotions and psychodemographic profiles from user-generated content. We examine several user attributes, including gender, income, political views, age, education, optimism, and life satisfaction. We correlate these predicted demographics with the emotional profiles emanating from user tweets, as captured by Ekman's emotion classification. We find that some users tend to express significantly more joy and significantly less sadness in their tweets, such as those predicted to be in a relationship, with children, or with a higher than average annual income or educational level. Users predicted to be women tend to be more opinionated, whereas those predicted to be men tend to be more neutral. Finally, users predicted to be younger and liberal tend to project more negative opinions and emotions. We discuss the implications of our findings to online privacy concerns and self-disclosure behavior.

Twitter-Augmented Journal Club: Educational Engagement and Experience So Far.

Science.gov (United States)

Udani, Ankeet D; Moyse, Daniel; Peery, Charles Andrew; Taekman, Jeffrey M

2016-04-15

Social media is a nascent medical educational technology. The benefits of Twitter include (1) easy adoption; (2) access to experts, peers, and patients across the globe; (3) 24/7 connectivity; (4) creation of virtual, education-based communities using hashtags; and (5) crowdsourcing information using retweets. We report on a novel Twitter-augmented journal club for anesthesia residents: its design, implementation, and impact. Our inaugural anesthesia Twitter-augmented journal club succeeded in engaging the anesthesia community and increasing residents' professional use of Twitter. Notably, our experience suggests that anesthesia residents are willing to use social media for their education.

Detecting suicidality on Twitter

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Bridianne O'Dea

2015-05-01

Full Text Available Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. However, validated and reliable methods are not yet fully established. This study aimed to examine whether the level of concern for a suicide-related post on Twitter could be determined based solely on the content of the post, as judged by human coders and then replicated by machine learning. From 18th February 2014 to 23rd April 2014, Twitter was monitored for a series of suicide-related phrases and terms using the public Application Program Interface (API. Matching tweets were stored in a data annotation tool developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO. During this time, 14,701 suicide-related tweets were collected: 14% were randomly (n = 2000 selected and divided into two equal sets (Set A and B for coding by human researchers. Overall, 14% of suicide-related tweets were classified as ‘strongly concerning’, with the majority coded as ‘possibly concerning’ (56% and the remainder (29% considered ‘safe to ignore’. The overall agreement rate among the human coders was 76% (average κ = 0.55. Machine learning processes were subsequently applied to assess whether a ‘strongly concerning’ tweet could be identified automatically. The computer classifier correctly identified 80% of ‘strongly concerning’ tweets and showed increasing gains in accuracy; however, future improvements are necessary as a plateau was not reached as the amount of data increased. The current study demonstrated that it is possible to distinguish the level of concern among suicide-related tweets, using both human coders and an automatic machine classifier. Importantly, the machine classifier replicated the accuracy of the human coders. The findings confirmed that Twitter is used by individuals to express suicidality and that such posts evoked a level of concern that warranted

Bibliographic Analysis of Nature Based on Twitter and Facebook Altmetrics Data.

Science.gov (United States)

Xia, Feng; Su, Xiaoyan; Wang, Wei; Zhang, Chenxin; Ning, Zhaolong; Lee, Ivan

2016-01-01

This paper presents a bibliographic analysis of Nature articles based on altmetrics. We assess the concern degree of social users on the Nature articles through the coverage analysis of Twitter and Facebook by publication year and discipline. The social media impact of a Nature article is examined by evaluating the mention rates on Twitter and on Facebook. Moreover, the correlation between tweets and citations is analyzed by publication year, discipline and Twitter user type to explore factors affecting the correlation. The results show that Twitter users have a higher concern degree on Nature articles than Facebook users, and Nature articles have higher and faster-growing impact on Twitter than on Facebook. The results also show that tweets and citations are somewhat related, and they mostly measure different types of impact. In addition, the correlation between tweets and citations highly depends on publication year, discipline and Twitter user type.

Detecting Bots on Russian Political Twitter.

Science.gov (United States)

Stukal, Denis; Sanovich, Sergey; Bonneau, Richard; Tucker, Joshua A

2017-12-01

Automated and semiautomated Twitter accounts, bots, have recently gained significant public attention due to their potential interference in the political realm. In this study, we develop a methodology for detecting bots on Twitter using an ensemble of classifiers and apply it to study bot activity within political discussions in the Russian Twittersphere. We focus on the interval from February 2014 to December 2015, an especially consequential period in Russian politics. Among accounts actively Tweeting about Russian politics, we find that on the majority of days, the proportion of Tweets produced by bots exceeds 50%. We reveal bot characteristics that distinguish them from humans in this corpus, and find that the software platform used for Tweeting is among the best predictors of bots. Finally, we find suggestive evidence that one prominent activity that bots were involved in on Russian political Twitter is the spread of news stories and promotion of media who produce them.

Use of twitter and Facebook by top European museums

OpenAIRE

Kostas Zafiropoulos; Vasiliki Vrana; Konstantinos Antoniadis

2015-01-01

With social media becoming so pervasive, museums strive to adopt them for their own use. Effective use of social media especially Facebook and Twitter seems to be promising. Social media offer museums the possibility to engage audiences, potential and active visitors with their collections and ideas. Facebook and Twitter are the market leaders of social media. This paper records the top European museums and their Facebook and Twitter accounts. It records the use of the two media, and by apply...

Identity Use and Misuse of Public Persona on Twitter

OpenAIRE

Köse, Dicle Berfin; Veijalainen, Jari; Semenov, Alexander

2016-01-01

Social media sites have appeared during the last 10 years and their use has exploded all over the world. Twitter is a microblogging service that has currently 320 million user profiles and over 100 million daily active users. Many celebrities and leading politicians have a verified profile on Twitter, including Justin Bieber, president Obama, and the Pope. In this paper we investigate the '‘hundreds of Putins and Obamas phenomenon’ on Twitter. We collected two data sets in 2015 co...

Promoviendo la investigación en salud con Twitter

Directory of Open Access Journals (Sweden)

Walter H. Curioso

2011-07-01

Full Text Available Objetivo: Discutir el uso de Twitter como una herramienta para la investigación y la promoción de la investigación, y describir la experiencia con la cuenta en Twitter de la Oficina de Promoción a la Investigación de la Universidad Peruana Cayetano Heredia (UPCH. Material y métodos: Para el primer objetivo, se realizó una búsqueda bibliográfica en MEDLINE, LILACS, LIPECS y Google Académico sobre el uso de Twitter y su aplicación en investigación y promoción de la investigación. Para el segundo objetivo, se realizó un análisis descriptivo de la base de datos de la cuenta en Twitter de la Oficina de Promoción a la Investigación de la UPCH. Resultados: Twitter es un servicio en línea gratuito, de fácil uso, el cual permite enviar y recibir mensajes cortos de hasta 140 caracteres (llamados "tweets". Twitter permite la comunicación casi en tiempo real y ha sido utilizado en investigación en salud para la vigilancia, educación y como una herramienta para la promoción, prevención y soporte al tratamiento de diversas condiciones. Asimismo, la experiencia de la Oficina de Promoción a la Investigación muestra que Twitter es un medio eficiente para la promoción de eventos, oportunidades de financiamiento y publicaciones relacionadas a la investigación. Conclusiones: Twitter es una herramienta importante con potencial para la investigación y la promoción de la investigación, como lo muestra el creciente número de publicaciones, y la experiencia preliminar de la Oficina de Promoción a la Investigación de la UPCH. (Rev Med Hered 2011;22:121-130.

The use and impact of Twitter at medical conferences: Best practices and Twitter etiquette.

Science.gov (United States)

Pemmaraju, Naveen; Mesa, Ruben A; Majhail, Navneet S; Thompson, Michael A

2017-10-01

The use of social media, and in particular, Twitter, for professional use among healthcare providers is rapidly increasing across the world. One medical subspecialty that is leading the integration of this new platform for communication into daily practice and for information dissemination to the general public is the field of hematology/oncology. A growing amount of research in this area demonstrates that there is increasing interest among physicians to learn not only how to use social media for consumption of educational material, but also how to generate and contribute original content in one's interest/expert areas. One aspect in which this phenomenon has been highlighted is at the time of maximum new information presentation: at a major medical conference. Hematologists/oncologists are engaging regularly in one of the most common forms of social media, Twitter, during major medical conferences, for purposes of debate, discussion, and real-time evaluation of the data being presented. As interest has grown in this area, this article aims to review the new norms, practices, and impact of using Twitter at the time of medical conferences, and also explores some of the barriers and pitfalls that users are encountering in this emerging field. Copyright © 2017. Published by Elsevier Inc.

OMG Earthquake! Can Twitter improve earthquake response?

Science.gov (United States)

Earle, P. S.; Guy, M.; Ostrum, C.; Horvath, S.; Buckmaster, R. A.

2009-12-01

The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public, text messages, can augment its earthquake response products and the delivery of hazard information. The goal is to gather near real-time, earthquake-related messages (tweets) and provide geo-located earthquake detections and rough maps of the corresponding felt areas. Twitter and other social Internet technologies are providing the general public with anecdotal earthquake hazard information before scientific information has been published from authoritative sources. People local to an event often publish information within seconds via these technologies. In contrast, depending on the location of the earthquake, scientific alerts take between 2 to 20 minutes. Examining the tweets following the March 30, 2009, M4.3 Morgan Hill earthquake shows it is possible (in some cases) to rapidly detect and map the felt area of an earthquake using Twitter responses. Within a minute of the earthquake, the frequency of “earthquake” tweets rose above the background level of less than 1 per hour to about 150 per minute. Using the tweets submitted in the first minute, a rough map of the felt area can be obtained by plotting the tweet locations. Mapping the tweets from the first six minutes shows observations extending from Monterey to Sacramento, similar to the perceived shaking region mapped by the USGS “Did You Feel It” system. The tweets submitted after the earthquake also provided (very) short first-impression narratives from people who experienced the shaking. Accurately assessing the potential and robustness of a Twitter-based system is difficult because only tweets spanning the previous seven days can be searched, making a historical study impossible. We have, however, been archiving tweets for several months, and it is clear that significant limitations do exist. The main drawback is the lack of quantitative information

Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

KAUST Repository

Salas-Zá rate, Marí a Pilar; Paredes-Valverde, Mario André s; Rodriguez-Garcia, Miguel Angel; Valencia-Garcí a, Rafael; Alor-Herná ndez, Giner

2017-01-01

in the Social Media (e.g. forums, blogs, and social networks) concerning to users’ opinions about experiences buying products and hiring services. Sentiment analysis or opinion mining is the field of study that analyses people’s opinions and mood from written

Building automatic customer complaints filtering application based on Twitter in Bahasa Indonesia

Science.gov (United States)

Gunawan, D.; Siregar, R. P.; Rahmat, R. F.; Amalia, A.

2018-03-01

Twitter has become a media to provide communication between a company with its customers. The average number of Twitter active users monthly is 330 million. A lot of companies realize the potential of Twitter to establish good relationship with their customers. Therefore, they usually have one official Twitter account to act as customer care division. In Indonesia, one of the company that utilizes the potential of Twitter to reach their customers is PT Telkom. PT Telkom has an official customer service account (called @TelkomCare) to receive customers’ problem. However, because of this account is open for public, Twitter users might post all kind of messages (not only complaints) to Telkom Care account. This leads to a problem that the Telkom Care account contains not only the customer complaints but also compliment and ordinary message. Furthermore, the complaints should be distributed to relevant division such as “Indihome”, “Telkomsel”, “UseeTV”, and “Telepon” based on the content of the message. This research built the application that automatically filter twitter post messages into several pre-defined categories (based on existing divisions) using Naïve Bayes algorithm. This research is done by collecting Twitter message, data cleaning, data pre-processing, training and testing data, and evaluate the classification result. This research yields 97% accuracy to classify Twitter message into the categories mentioned earlier.

A Descriptive Analysis of the Use of Twitter by Emergency Medicine Residency Programs.

Science.gov (United States)

Diller, David; Yarris, Lalena M

2018-02-01

Twitter is increasingly recognized as an instructional tool by the emergency medicine (EM) community. In 2012, the Council of Residency Directors in Emergency Medicine (CORD) recommended that EM residency programs' Twitter accounts be managed solely by faculty. To date, little has been published regarding the patterns of Twitter use by EM residency programs. We analyzed current patterns in Twitter use among EM residency programs with accounts and assessed conformance with CORD recommendations. In this mixed methods study, a 6-question, anonymous survey was distributed via e-mail using SurveyMonkey. In addition, a Twitter-based search was conducted, and the public profiles of EM residency programs' Twitter accounts were analyzed. We calculated descriptive statistics and performed a qualitative analysis on the data. Of 168 Accreditation Council for Graduate Medical Education-accredited EM programs, 88 programs (52%) responded. Of those programs, 58% (51 of 88) reported having a program-level Twitter account. Residents served as content managers for those accounts in the majority of survey respondents (61%, 28 of 46). Most programs did not publicly disclose the identity or position of their Twitter content manager. We found a wide variety of applications for Twitter, with EM programs most frequently using Twitter for educational and promotional purposes. There is significant variability in the numbers of followers for EM programs' Twitter accounts. Applications and usage among EM residency programs are varied, and are frequently not consistent with current CORD recommendations.

FCJ-190 Building a Better Twitter: A Study of the Twitter Alternatives GNU social, Quitter, rstat.us, and Twister

Directory of Open Access Journals (Sweden)

Robert W. Gehl

2015-06-01

Full Text Available Drawing on interviews with developers and close readings of site interfaces and architectures, this essay explores four Twitter alternatives: Twister, rstat.us, GNU social (a Free Software Foundation microblogging software project and Quitter (a specific installation of GNU social. The interviews and analyses of these Twitter alternatives reveal how these developers relate their projects to mainstream social media architectures and cultures; how they conceive of Twitter’s development over time; how they think about legal issues as they make their alternatives; and whether or not they conceive of their work as activism. In sum, I explore how these developers are critically reverse engineering Twitter and how that process intersects with multiple concerns and tensions, and how these tensions are generating new ways to think about what it means to do microblogging.

Content Analysis of Tobacco-related Twitter Posts

OpenAIRE

Mysl?n, Mark; Zhu, Shu-Hong; Conway, Michael

2013-01-01

Objective We present results of a content analysis of tobacco-related Twitter posts (tweets), focusing on tweets referencing e-cigarettes and hookah. Introduction Vast amounts of free, real-time, localizable Twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particularly in emerging areas of public health concern where reliable statistical evidence is not readily accessible. Existing appli...

EFFECT OF A NOVEL ENGAGEMENT STRATEGY USING TWITTER ON TEST PERFORMANCE

Directory of Open Access Journals (Sweden)

Amanda L. Webb

2015-11-01

Full Text Available Introduction: Medical educators in recent years have been using social media for more penetrance to technologically-savvy learners. The utility of using Twitter for curriculum content delivery has not been studied. We sought to determine if participation in a social media-based educational supplement would improve student performance on a test of clinical images at the end of the semester. Methods: 116 second-year medical students were enrolled in a lecture-based clinical medicine course, in which images of common clinical exam findings were presented. An additional, optional assessment was performed on Twitter. Each week, a clinical presentation and physical exam image (not covered in course lectures were distributed via Twitter, and students were invited to guess the exam finding or diagnosis. After the completion of the course, students were asked to participate in a slideshow “quiz” with 24 clinical images, half from lecture and half from Twitter. Results: We conducted a one-way analysis of variance to determine the effect Twitter participation had on total, Twitter-only, and lecture-only scores. Twitter participation data was collected from the end-of-course survey and was defined as submitting answers to the Twitter-only questions “all or most of the time”, “about half of the time”, and “little or none of the time.” We found a significant difference in overall scores (p<0.001 and in Twitter-only scores (p<0.001. There was not enough evidence to conclude a significant difference in lecture-only scores (p=0.124. Students who submitted answers to Twitter “all or most of the time” or “about half the time” had significantly higher overall scores and Twitter-only scores (p<0.001 and p<0.001, respectively than those students who only submitted answers “little or none of the time.” Conclusion: While students retained less information from Twitter than from traditional classroom lecture, some retention was noted. Future

Mining Twitter Data Stream to Augment NASA GPM Validation

Science.gov (United States)

Teng, W. L.; Albayrak, A.; Huffman, G. J.; Vollmer, B.

2017-12-01

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value

EXTENDED SPEECH EMOTION RECOGNITION AND PREDICTION

Directory of Open Access Journals (Sweden)

Theodoros Anagnostopoulos

2014-11-01

Full Text Available Humans are considered to reason and act rationally and that is believed to be their fundamental difference from the rest of the living entities. Furthermore, modern approaches in the science of psychology underline that humans as a thinking creatures are also sentimental and emotional organisms. There are fifteen universal extended emotions plus neutral emotion: hot anger, cold anger, panic, fear, anxiety, despair, sadness, elation, happiness, interest, boredom, shame, pride, disgust, contempt and neutral position. The scope of the current research is to understand the emotional state of a human being by capturing the speech utterances that one uses during a common conversation. It is proved that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers. The paper deals with emotion classification by a set of majority voting classifiers that combines three certain types of basic classifiers with low computational complexity. The basic classifiers stem from different theoretical background in order to avoid bias and redundancy which gives the proposed set of classifiers the ability to generalize in the emotion domain space.

Using Twitter to communicate conservation science from a professional conference.

Science.gov (United States)

Bombaci, Sara P; Farr, Cooper M; Gallo, H Travis; Mangan, Anna M; Stinson, Lani T; Kaushik, Monica; Pejchar, Liba

2016-02-01

Scientists are increasingly using Twitter as a tool for communicating science. Twitter can promote scholarly discussion, disseminate research rapidly, and extend and diversify the scope of audiences reached. However, scientists also caution that if Twitter does not accurately convey science due to the inherent brevity of this media, misinformation could cascade quickly through social media. Data on whether Twitter effectively communicates conservation science and the types of user groups receiving these tweets are lacking. To address these knowledge gaps, we examined live tweeting as a means of communicating conservation science at the 2013 International Congress for Conservation Biology (ICCB). We quantified and compared the user groups sending and reading live tweets. We also surveyed presenters to determine their intended audiences, which we compared with the actual audiences reached through live tweeting. We also asked presenters how effectively tweets conveyed their research findings. Twitter reached 14 more professional audience categories relative to those attending and live tweeting at ICCB. However, the groups often reached through live tweeting were not the presenters' intended audiences. Policy makers and government and non-governmental organizations were rarely reached (0%, 4%, and 6% of audience, respectively), despite the intent of the presenters. Plenary talks were tweeted about 6.9 times more than all other oral or poster presentations combined. Over half the presenters believed the tweets about their talks were effective. Ineffective tweets were perceived as vague or missing the presenters' main message. We recommend that presenters who want their science to be communicated accurately and broadly through Twitter should provide Twitter-friendly summaries that incorporate relevant hashtags and usernames. Our results suggest that Twitter can be used to effectively communicate speakers' findings to diverse audiences beyond conference walls. © 2015

Twitter for Good Change the World One Tweet at a Time

CERN Document Server

Diaz-Ortiz, Claire

2011-01-01

Silver Medal Winner, Social Networking, 2012 Axiom Business Book AwardsSilver Medal Winner, Business and Leadership, 2012 Nautilus Book Awards The official word from Twitter on how to harness the power of the platform for any cause. As recent events in Japan, the Middle East, and Haiti have shown, Twitter offers a unique platform to connect individuals and influence change in ways that were unthinkable only a short time ago. In Twitter for Good, Claire Diaz Ortiz, Twitter's head of corporate social innovation and philanthropy, shares the same strategies she offers to organizations launching

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity.

Science.gov (United States)

Nguyen, Quynh C; Li, Dapeng; Meng, Hsien-Wen; Kath, Suraj; Nsoesie, Elaine; Li, Feifei; Wen, Ming

2016-10-17

Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors. We utilized Twitter's streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast

ANTECEDENTS OF INTERPERSONAL COMMUNICATION MOTIVES ON TWITTER: LONELINESS AND LIFE SATISFACTION

Directory of Open Access Journals (Sweden)

Yoosun Hwang

2014-06-01

Full Text Available As the sharp distinction between face-to-face communication and mediated interpersonal communication is disappearing, Twitter is now being used for private and public exchanges. This study aims to explore interpersonal communication motives on Twitter in relation to individuals’ social psychological states of loneliness and life satisfaction. Social compensation and social-enhancement hypotheses were considered for the theoretical background. Data were gathered from Twitter users through online surveys. Hierarchical regression analyses on each communication motive on Twitter (pleasure, affection, inclusion, escape, relaxation, and control were performed. Results revealed that loneliness negatively affected the motives of pleasure and affection, while life satisfaction positively affected the motives of pleasure, affection, relaxation, and control. The implications of these findings and the meaning of Twitter for interpersonal communication are discussed.

A content analysis of chronic diseases social groups on Facebook and Twitter.

Science.gov (United States)

De la Torre-Díez, Isabel; Díaz-Pernas, Francisco Javier; Antón-Rodríguez, Míriam

2012-01-01

Research on the use of social networks for health-related purposes is limited. This study aims to characterize the purpose and use of Facebook and Twitter groups concerning colorectal cancer, breast cancer, and diabetes. We searched in Facebook ( www.facebook.com ) and Twitter ( www.twitter.com ) using the terms "colorectal cancer," "breast cancer," and "diabetes." Each important group has been analyzed by extracting its network name, number of members, interests, and Web site URL. We found 216 breast cancer groups, 171 colorectal cancer groups, and 527 diabetes groups on Facebook and Twitter. The largest percentage of the colorectal cancer groups (25.58%) addresses prevention, similarly to breast cancer, whereas diabetes groups are mainly focused on research issues (25.09%). There are more social groups about breast cancer and diabetes on Facebook (around 82%) than on Twitter (around 18%). Regarding colorectal cancer, the difference is less: Facebook had 62.23%, and Twitter 31.76%. Social networks are a useful tool for supporting patients suffering from these three diseases. Regarding the use of these social networks for disease support purposes, Facebook shows a higher usage rate than Twitter, perhaps because Twitter is newer than Facebook, and its use is not so generalized.

Engagement through Microblogging: Educator Professional Development via Twitter

Science.gov (United States)

Carpenter, Jeffrey P.; Krutka, Daniel G.

2015-01-01

Traditional, top-down professional development (PD) can render teachers mere implementers of the ideas of others, but there is some hope that the participatory nature of social media such as Twitter might support more grassroots PD. To better understand Twitter's role in education, we conducted a survey of K-16 educators regarding their use of the…

Large-scale Comparative Sentiment Analysis of News Articles

OpenAIRE

Wanner, Franz; Rohrdantz, Christian; Mansmann, Florian; Stoffel, Andreas; Oelke, Daniela; Krstajic, Milos; Keim, Daniel; Luo, Dongning; Yang, Jing; Atkinson, Martin

2009-01-01

Online media offers great possibilities to retrieve more news items than ever. In contrast to these technical developments, human capabilities to read all these news items have not increased likewise. To bridge this gap, this poster presents a visual analytics tool for conducting semi-automatic sentiment analysis of large news feeds. The tool retrieves and analyzes the news of two categories (Terrorist Attack and Natural Disasters) and news which belong to both categories of the Europe Media ...

Assessing sentiment in conflict zones through social media

OpenAIRE

Bourret, Andrew K.; Wines, Joshua D.; Mendes, Jason M.

2016-01-01

Approved for public release; distribution is unlimited While it is widely accepted that polling can assess levels of popular support in a geographic area by surveying a cross-segment of its population, it is less well accepted that analysts can use social media analysis to assess sentiment or popular support within the same space. We examine this question by comparing geographically anchored polling and social media data, utilizing over 1.4 million geo-referenced messages sent through the ...