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.
Similarly, REF present an approach in which a rule-based classifier based on a polarity lexicon and a supervised classifier trained on in-domain data are combined.
When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging
Annual Meeting Of The Association For Computational Linguistics
Computer Science