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

ACL ID P11-1014
Title Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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 sen...