Results 1 - 10
of
68
Sentiment strength detection for the social web
- Journal of the American Society for Information Science and Technology
, 2012
"... Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twit ..."
Abstract
-
Cited by 62 (6 self)
- Add to MetaCart
(Show Context)
Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited for this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
Sentiment Analysis of Short Informal Texts
, 2014
"... Abstract We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statist ..."
Abstract
-
Cited by 21 (4 self)
- Add to MetaCart
Abstract We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surfaceform, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task 'Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-ofthe-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.
A large-scale sentiment analysis for yahoo! answers
- In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM ’12, ACM
, 2012
"... Sentiment extraction from online web documents has re-cently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limi ..."
Abstract
-
Cited by 20 (2 self)
- Add to MetaCart
(Show Context)
Sentiment extraction from online web documents has re-cently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not inves-tigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investi-gate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sen-timents in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user’s ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in ad-vertising, recommendation, and search.
Heart and Soul: Sentiment Strength Detection in the Social Web with SentiStrength 1
"... Emotions are important in communication to effectively convey messages and to understand reactions to messages. Large scale studies of communication need methods to detect sentiment in order to investigate or model the processes involved. This chapter describes the sentiment strength detection progr ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
(Show Context)
Emotions are important in communication to effectively convey messages and to understand reactions to messages. Large scale studies of communication need methods to detect sentiment in order to investigate or model the processes involved. This chapter describes the sentiment strength detection program SentiStrength that was developed during the CyberEmotions project to detect the strength of sentiments expressed in social web texts. SentiStrength uses a lexical approach that exploits a list of sentiment-related terms and has rules to deal with standard linguistic and social web methods to express sentiment, such as emoticons, exaggerated punctuation and deliberate misspellings. This chapter also describes how SentiStrength can be refined for particular topics and contexts and how variants are created for different languages. The chapter also briefly describes some studies that have applied SentiStrength to analyse trends in Twitter and You Tube comments.
Tweeting Links to Academic Articles
"... Academic articles are now frequently tweeted and so Twitter seems to be a useful tool for scholars to use to help keep up with publications and discussions in their fields. Perhaps as a result of this, tweet counts are increasingly used by digital libraries and journal websites as indicators of an a ..."
Abstract
-
Cited by 7 (3 self)
- Add to MetaCart
Academic articles are now frequently tweeted and so Twitter seems to be a useful tool for scholars to use to help keep up with publications and discussions in their fields. Perhaps as a result of this, tweet counts are increasingly used by digital libraries and journal websites as indicators of an article’s interest or impact. Nevertheless, it is not known whether tweets are typically positive, neutral or critical, or how articles are normally tweeted. These are problems for those wishing to tweet articles effectively and for those wishing to know whether tweet counts in digital libraries should be taken seriously. In response, a pilot study content analysis was conducted of 270 tweets linking to articles in four journals, four digital libraries and two DOI URLs, collected over a period of eight months in 2012. The vast majority of the tweets echoed an article title (42%) or a brief summary (41%). One reason for summarising an article seemed to be to translate it for a general audience. Few tweets explicitly praised an article and none were critical. Most tweets did not directly refer to the article author, but some did and others were clearly self-citations. In summary, tweets containing links to scholarly articles generally provide little more than publicity, and so whilst tweet counts may provide evidence of the popularity of an article, the contents of the tweets themselves are unlikely to give deep insights into scientists' reactions to publications, except perhaps in special cases.
Enhancing Music Recommender Systems with Personality Information and Emotional States:
"... Abstract. This position paper describes the initial research assump-tions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. We will give attention to how people ..."
Abstract
-
Cited by 7 (4 self)
- Add to MetaCart
(Show Context)
Abstract. This position paper describes the initial research assump-tions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. We will give attention to how people use music to regulate their emotional states, and how this regulation is related to their personality. Furthermore, we will focus on how to acquire data from social media (i.e., microblogging sites such as Twitter) to predict the current emotional state of users. Finally, we will discuss how we plan to connect the correct emotionally laden music pieces to support the emotion regulation style of users.
Emotions in product reviews – Empirics and models
- Proceedings of 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, PASSAT/SocialCom
, 2011
"... Abstract-Online communities provide Internet users with means to overcome some information barriers and constraints, such as the difficulty to gather independent information about products and firms. Product review communities allow customers to share their opinions and emotions after the purchase ..."
Abstract
-
Cited by 6 (5 self)
- Add to MetaCart
(Show Context)
Abstract-Online communities provide Internet users with means to overcome some information barriers and constraints, such as the difficulty to gather independent information about products and firms. Product review communities allow customers to share their opinions and emotions after the purchase of a product. We introduce a new dataset of product reviews from Amazon.com, with emotional information extracted by sentiment detection tools. Our statistical analysis of this data provides evidence for the existence of polemic reviews, as well as for the coexistence of positive and negative emotions inside reviews. We find a strong bias towards large values in the expression of positive emotions, while negative ones are more evenly distributed. We identified different time dynamics of the creation of reviews dependent on the presence of marketing and word of mouth effects. We define an agent-based model of the users of product review communities using a modeling framework for online emotions. This model can reproduce the scenarios of response to external influences, as well as some properties of the distributions of positive and negative emotions expressed in product reviews. This analysis and model can provide guidelines to manufacturers on how to increase customer satisfaction and how to measure the emotional impact of marketing campaigns through reviews data.
An Argument-based Approach to Mining Opinions from Twitter ⋆
"... Abstract. Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have ..."
Abstract
-
Cited by 5 (0 self)
- Add to MetaCart
(Show Context)
Abstract. Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have
Emotional divergence influences information spreading
- in Twitter,” in AAAI ICWSM 2012, 2012
"... We analyze data about the micro-blogging site Twitter using sentiment extraction techniques. From an information per-spective, Twitter users are involved mostly in two processes: information creation and subsequent distribution (tweeting), and pure information distribution (retweeting), with pro-nou ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
We analyze data about the micro-blogging site Twitter using sentiment extraction techniques. From an information per-spective, Twitter users are involved mostly in two processes: information creation and subsequent distribution (tweeting), and pure information distribution (retweeting), with pro-nounced preference to the first. However a rather substantial fraction of tweets are retweeted. Here, we address the role of the sentiment expressed in tweets for their potential after-math. We find that although the overall sentiment (polarity) does not influence the probability of a tweet to be retweeted, a new measure called emotional divergence does have an im-pact. In general, tweets with high emotional diversity have a better chance of being retweeted, hence influencing the dis-tribution of information. 1
Political polarization and popularity in online participatory media.
- In PLEAD,
, 2012
"... ABSTRACT We present our approach to online popularity and its applications to political science, aiming at the creation of agentbased models that reproduce patterns of popularity in participatory media. We illustrate our approach analyzing a dataset from Youtube, composed of the view statistics and ..."
Abstract
-
Cited by 5 (4 self)
- Add to MetaCart
(Show Context)
ABSTRACT We present our approach to online popularity and its applications to political science, aiming at the creation of agentbased models that reproduce patterns of popularity in participatory media. We illustrate our approach analyzing a dataset from Youtube, composed of the view statistics and comments for the videos of the U.S. presidential campaigns of 2008 and 2012. Using sentiment analysis, we quantify the collective emotions expressed by the viewers, finding that democrat campaigns elicited more positive collective emotions than republican campaigns. Techniques from computational social science allow us to measure virality of the videos of each campaign, to find that democrat videos are shared faster but republican ones are remembered longer inside the community. Last we present our work in progress in voting advice applications, and our results analyzing the data from choose4greece.com. We show how we assess the policy differences between parties and their voters, and how voting advice applications can be extended to test our agentbased models.
