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

Abstract

We describe a sentiment classication method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to nd the association between words that express similar sentiments in different domains. The created thesaurus is then used to expand feature vectors to train a binary classier. Unlike previous cross-domain sentiment classication methods, our method can efciently learn from multiple source domains. Our method significantly outperforms numerous baselines and returns results that are better than or comparable to previous cross-domain sentiment classication methods on a benchmark dataset containing Amazon user reviews for different types of products.