Interpreting the Public Sentiment Variations on Twitter

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Name Interpreting the Public Sentiment Variations on Twitter
Technology Dot net, MS SQL
Category Data Mining
Description 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
(LDA) 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. To further enhance the readability
of the mined reasons, we select the most representative
tweets for foreground topics and develop another
generative model called Reason Candidate and
Background LDA (RCB-LDA) to rank them with
respect to their “popularity” within the variation
period. Experimental results show that our methods can
effectively find foreground topics and rank reason candidates. The proposed models can also be applied to
other tasks such as finding topic differences between
two sets of documents.
IEEE Paper Yes
IEEE Paper Year 2014
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