Paper: Discourse Connectors for Latent Subjectivity in Sentiment Analysis

ACL ID N13-1100
Title Discourse Connectors for Latent Subjectivity in Sentiment Analysis
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Document-level sentiment analysis can bene- fit from fine-grained subjectivity, so that sen- timent polarity judgments are based on the relevant parts of the document. While fine- grained subjectivity annotations are rarely available, encouraging results have been ob- tained by modeling subjectivity as a latent variable. However, latent variable models fail to capitalize on our linguistic knowledge about discourse structure. We present a new method for injecting linguistic knowledge into latent variable subjectivity modeling, using discourse connectors. Connector-augmented transition features allow the latent variable model to learn the relevance of discourse con- nectors for subjectivity transitions, without subjectivity annotations. This yields signif- icantly improved performance on doc...