A comparative study of Bayesian models for unsupervised sentiment detection

Chenghua Lin, Yulan He, Richard Everson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Citations (Scopus)

Abstract

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing 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 sentiment topic detection.
Original languageEnglish
Title of host publicationThe 14th Conference on Computational Natural Language Learning (CoNLL-2010)
PublisherAssociation for Computational Linguistics
Pages144-152
Number of pages9
ISBN (Print)978-1-932432-83-1
Publication statusPublished - 2010

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    Lin, C., He, Y., & Everson, R. (2010). A comparative study of Bayesian models for unsupervised sentiment detection. In The 14th Conference on Computational Natural Language Learning (CoNLL-2010) (pp. 144-152). Association for Computational Linguistics. http://aclweb.org/anthology//W/W10/W10-2918.pdf