A comparative study of bayesian models for unsupervised sentiment detection

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

View graph of relations Save citation


Research units


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 sentiment-topic (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.

Request a copy

Request a copy


Publication date1 Jan 2010
Publication titleProceeding : CoNLL '10 proceedings of the fourteenth conference on computational natural language learning
EditorsMirella Lapata
Place of PublicationStroudsburg, PA (US)
PublisherAssociation for Computational Linguistics
Number of pages9
ISBN (Print)978-1-932432-83-1
Original languageEnglish
Event14th Conference on Computational Natural Language Learning, CoNLL'10 - Uppsala, Sweden
Duration: 15 Jul 201016 Jul 2010


Conference14th Conference on Computational Natural Language Learning, CoNLL'10

Employable Graduates; Exploitable Research

Copy the text from this field...