Paper: Harnessing WordNet Senses for Supervised Sentiment Classification

ACL ID D11-1100
Title Harnessing WordNet Senses for Supervised Sentiment Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

Traditional approaches to sentiment classifica- tion rely on lexical features, syntax-based fea- tures or a combination of the two. We pro- pose semantic features using word senses for a supervised document-level sentiment classi- fier. To highlight the benefit of sense-based features, we compare word-based representa- tion of documents with a sense-based repre- sentation where WordNet senses of the words are used as features. In addition, we highlight the benefit of senses by presenting a part-of- speech-wise effect on sentiment classification. Finally, we show that even if a WSD engine disambiguates between a limited set of words in a document, a sentiment classifier still per- forms better than what it does in absence of sense annotation. Since word senses used as features show promise,...