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Keywords:

  • information discovery;
  • linguistic analysis;
  • knowledge discovery

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.