Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 132–141, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification Danushka Bollegala The University of Tokyo 7-3-1, Hongo, Tokyo, 113-8656, Japan danushka@ iba.t.u-tokyo.ac.jp David Weir School of Informatics University of Sussex Falmer, Brighton, BN1 9QJ, UK d.j.weir@ sussex.ac.uk John Carroll School of Informatics University of Sussex Falmer, Brighton, BN1 9QJ, UK j.a.carroll@ sussex.ac.uk Abstract We describe a sentiment classification method that is applicable when we do not have any la- beled data for a target domain but have some labeled data for multiple other domains, des- ignated as the source domains. We automat- ically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to find the associa- tion between words that express similar senti- ments in different domains. The created the- saurus is then used to expand feature vectors to train a binary classifier. Unlike previous cross-domain sentiment classification meth- ods, our method can efficiently learn from multiple source domains. Our method signif- icantly outperforms numerous baselines and returns results that are better than or com- parable to previous cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products. 1 Introduction Users express opinions about products or services they consume in blog posts, shopping sites, or re- view sites. It is useful for both consumers as well as for producers to know what general public think about a particular product or service. Automatic document level sentiment classification (Pang et al., 2002; Turney, 2002) is the task of classifying a given review with respect to the sentiment expressed by the author of the review. For example, a sentiment classifier might classify a user review about a movie as positive or negative depending on the sentiment expressed in the review. Sentiment classification has been applied in numerous tasks such as opinion mining (Pang and Lee, 2008), opinion summariza- tion (Lu et al., 2009), contextual advertising (Fan and Chang, 2010), and market analysis (Hu and Liu, 2004). Supervised learning algorithms that require la- beled data have been successfully used to build sen- timent classifiers for a specific domain (Pang et al., 2002). However, sentiment is expressed differently in different domains, and it is costly to annotate data for each new domain in which we would like to apply a sentiment classifier. For example, in the domain of reviews about electronics products, the words “durable” and “light” are used to express pos- itive sentiment, whereas “expensive” and “short bat- tery life” often indicate negative sentiment. On the other hand, if we consider the books domain the words “exciting” and “thriller” express positive sen- timent, whereas the words “boring” and “lengthy” usually express negative sentiment. A classifier trained on one domain might not perform well on a different domain because it would fail to learn the sentiment of the unseen words. Work in cross-domain sentiment …

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