Paper: A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection

ACL ID W10-2918
Title A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

This paper presents a comparative study of three closely related Bayesian mod- els for unsupervised document level senti- ment classification, namely, the latent sen- timent model (LSM), the joint sentiment- topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie re- view dataset and the multi-domain senti- ment dataset. It has been found that while all the three models achieve either bet- ter or comparable performance on these two corpora when compared to the exist- ing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint senti- ment topic detection....