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

This study presents a novel approach to the problem of system portability across different 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 WordNet. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Alina Andreevskaia, Sabine Bergler
• Keywords: ACL 2008, Annotated In-domain Data, Computational Linguistics, Corpus-based Classifier, In-domain Data