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首页> 外文期刊>Journal of the American Society for Information Science and Technology >Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative Influence of Popular Twitterers
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Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative Influence of Popular Twitterers

机译：Twitter观众的情感分析：衡量受欢迎的Twitter员工的正面或负面影响

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摘要

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.
机译：Twitter是一种流行的微博服务，用于在140个字符以内的任何主题上读写数百万条短消息。受欢迎或有影响力的用户在推特上发布其状态，并被其用户转发，提及或回复。流行用户及其受众对推文的情感分析揭示了受众是否对流行用户有利。我们分析了超过300万条推文，其中提及或回复了13位最具影响力的用户，以确定受众群体的情绪。 Twitter消息反映了其最受欢迎的用户的情绪状况。我们使用情绪分析技术作为有效的人气指标或度量。首先，我们区分了流行用户的正面和负面受众。其次，我们发现受欢迎的用户在推文中表达的情感影响了他们的受众情感。第三，根据以上两个发现，我们针对这种影响开发了一种正负度量。最后，通过格兰杰因果关系分析，我们发现受众基于时间序列的正负情绪变化与流行用户的真实世界的情绪状况有关。我们认为，受欢迎的用户及其受众之间的正负影响力度量提供了对用户影响力的新见解，并且与现实世界有关。

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