Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Exploring the characteristics of opinion expressions for political opinion classification
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Journal of the American Society for Information Science and Technology
Lexicon-based methods for sentiment analysis
Computational Linguistics
Influence and passivity in social media
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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Twitter is a popular microblogging service that is used to read and write millions of short messages on any topic within a 140-character limit. Popular or influential users tweet their status and are retweeted, mentioned, or replied to by their audience. Sentiment analysis of the tweets by popular users and their audience reveals whether the audience is favorable to popular users. We analyzed over 3,000,000 tweets mentioning or replying to the 13 most influential users to determine audience sentiment. Twitter messages reflect the landscape of sentiment toward its most popular users. We used the sentiment analysis technique as a valid popularity indicator or measure. First, we distinguished between the positive and negative audiences of popular users. Second, we found that the sentiments expressed in the tweets by popular users influenced the sentiment of their audience. Third, from the above two findings we developed a positive-negative measure for this influence. Finally, using a Granger causality analysis, we found that the time-series-based positive-negative sentiment change of the audience was related to the real-world sentiment landscape of popular users. We believe that the positive-negative influence measure between popular users and their audience provides new insights into the influence of a user and is related to the real world. © 2012 Wiley Periodicals, Inc.