The Open UniversitySkip to content

A comparative study of Bayesian models for unsupervised sentiment detection

Lin, Chenghua; He, Yulan and Everson, Richard (2010). A comparative study of Bayesian models for unsupervised sentiment detection. In: The 14th Conference on Computational Natural Language Learning (CoNLL-2010), 15-16 Jul 2010, Uppsala, Sweden.

Google Scholar: Look up in Google Scholar


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.

Item Type: Conference Item
Copyright Holders: 2010 Association for Computational Linguistics
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23784
Depositing User: Kay Dave
Date Deposited: 19 Oct 2010 11:58
Last Modified: 25 Mar 2016 04:54
Share this page:

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340