Paper: When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging

ACL ID P08-1034
Title When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

This study presents a novel approach to the problem of system portability across differ- ent domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on Word- Net. The paper explores the challenges of sys- tem portability across domains and text gen- res (movie reviews, news, blogs, and product reviews), highlights the factors affecting sys- tem performance on out-of-domain and small- set in-domain data, and presents a new sys- tem consisting of the ensemble of two classi- fiers with precision-based vote weighting, that provides significant gains in accuracy and re- call over the corpus-based classifier and the lexicon-based system taken individually.