A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection

Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 144–152, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection Chenghua Lin School of Engineering, Computing and Mathematics University of Exeter Exeter, EX4 4QF, UK. cl322@exeter.ac.uk Yulan He Knowledge Media Institute The Open University Milton Keynes MK7 6AA, UK Y.He@open.ac.uk Richard Everson School of Engineering, Computing and Mathematics University of Exeter Exeter, EX4 4QF, UK. R.E.Everson@exeter.ac.uk Abstract 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. 1 Introduction With the explosion of web 2.0, various types of social media such as blogs, discussion forums and peer-to-peer networks present a wealth of infor- mation that can be very helpful in assessing the general public’s sentiments and opinions towards products and services. Recent surveys have re- vealed that opinion-rich resources like online re- views are having greater economic impact on both consumers and companies compared to the tradi- tional media (Pang and Lee, 2008). Driven by the demand of gleaning insights of such great amounts of user-generated data, work on new methodologies for automated sentiment analysis has bloomed splendidly. Compared to the traditional topic-based text classification, sentiment classification is deemed to be more challenging as sentiment is often em- bodied in subtle linguistic mechanisms such as the use of sarcasm or incorporated with highly domain-specific information. Although the task of identifying the overall sentiment polarity of a document has been well studied, most of the work is highly domain dependent and favoured in supervised learning (Pang et al., 2002; Pang and Lee, 2004; Whitelaw et al., 2005; Kennedy and Inkpen, 2006; McDonald et al., 2007), re- quiring annotated corpora for every possible do- main of interest, which is impractical for real applications. Also, it is well-known that senti- ment classifiers trained on one domain often fail to produce satisfactory results when shifted to an- other domain, since sentiment expression can be quite different in different domains (Aue and Ga- mon, 2005). Moreover, aside from the diversity of genres and large-scale size of Web corpora, user-generated contents evolve rapidly over time, which demands much more efficient algorithms for sentiment analysis than the current approaches can offer. These observations have thus motivated the problem of using unsupervised approaches for domain-independent joint sentiment topic detec- tion. Some recent research efforts have been made …

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