In this abstract, we analyze public sentiment variations on Twitter and mine possible reasons behind such variations. To track public sentiment, we combine two state-of-the-art sentiment analysis tools to obtain sentiment information towards interested targets (e.g., “Obama”) in each tweet. Based on the sentiment label obtained for each tweet, we can track the public sentiment regarding the corresponding target using some descriptive statistics (e.g., Sentiment Percentage). We propose two Latent Dirichlet Allocation (LDA) based models to analyze tweets in significant variation periods, and infer possible reasons for the variations. The first model, called Foreground and Background LDA (FB-LDA), can filter out background topics and extract foreground topics from tweets in the variation period, with the help of an auxiliary set of background tweets generated just before the variation. To handle the last two challenges, we propose another generative model called Reason Candidate and Background LDA (RCB-LDA). RCB-LDA first extracts representative tweets for the foreground topics (obtained from FB-LDA) as reason candidates. Then it will associate each remaining tweet in the variation period with one reason candidate and rank the reason candidates by the number of tweets associated with them. Experimental results on real Twitter data show that our method can outperform baseline methods and effectively mine desired information behind public sentiment variations.
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