Do Topic-Dependent Models Improve Microblog Sentiment Estimation?


  • Francine Chen FX Palo Alto Laboratory, Inc.
  • Seyed Mirisaee Joseph Fourier University



sentiment, polarity, microblogs, topic-dependent


A topic-independent sentiment model is commonly used to estimate sentiment in microblogs. But for movie and product reviews, domain adaptation has been shown to improve sentiment estimation performance. We examined whether topic-dependent models improve polarity estimation of microblogs. We considered both a model trained on Twitter tweets containing a target keyword and a model trained on an enlarged set of tweets containing terms related to a topic.  Comparing the performance of the topic-dependent models to a topic-independent model trained on a general sample of  tweets, we noted that for some topics, topic-dependent models performed better. We then propose a method for predicting which topics are likely to have better sentiment estimation performance when a topic-dependent sentiment model is used.




How to Cite

Chen, F., & Mirisaee, S. (2014). Do Topic-Dependent Models Improve Microblog Sentiment Estimation?. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 575-578.