Paper: Multi-domain Sentiment Classification

ACL ID P08-2065
Title Multi-domain Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve perform- ance through fusing training data from multi- ple domains. To achieve this, we propose two approaches of fusion, feature-level and classi- fier-level, to use training data from multiple domains simultaneously. Experimental stud- ies show that multi-domain sentiment classi- fication using the classifier-level approach performs much better than single domain classification (using the training data indi- vidually).