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Sentiment analysis of the correlation between regular tweets and retweets

机译:常规推文与转发之间的相关性的情感分析

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In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets to measure the influence. We categorize the Twitter users in three different ways to investigate three factors, which are the number of followers, betweenness centrality and the types of accounts. Community detection and machine learning are integrated into our approach. We find that the difference for correlation coefficients exists between different levels of the number of followers, and different types of users. Our method sheds a light on better predicting the dynamics of tweets diffusion by including the sentiment factor into the prediction model.
机译:在本文中,我们研究了常规推文的情绪对转发的影响。我们提出了一种方法来计算每个推文和每个推特用户的情绪分数。此方法使我们能够将推文和转关放入同一时间段内以探索情绪因素。我们采用了常规推文的情绪分数与转派人员之间的相关系数来衡量影响。我们以三种不同的方式对Twitter用户进行分类,以调查三个因素,这些因素是追随者的数量,之间的中心性和账户类型。社区检测和机器学习集成到我们的方法中。我们发现,在不同级别的追随者和不同类型的用户之间存在相关系数的差异存在。我们的方法通过将语调因子列入预测模型,更好地预测推文扩散的动态更好地缩小了光。

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