Interpreting the Public Sentiment Variations on Twitter
Abstract— Millions of users share their opinions on Twitter, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. Therefore it has attracted attention in both academia and industry. Previous research mainly focused on modeling and tracking public sentiment. In this work, we move one step further to interpret sentiment variations. We observed that emerging topics (named foreground topics) within the sentiment variation periods are highly related to the genuine reasons behind the variations. Based on this observation, we propose a Latent Dirichlet Allocation < Final Year Projects 2016 > based model, Foreground and Background LDA (FB-LDA), to distill foreground topics and ﬁlter out longstanding background topics. These foreground topics can give potential interpretations of the sentiment variations .
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