The sentiment captured in opinionated text provides interesting and valuable information for social media services. However, due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment. We propose a semi-supervised framework for generating a domain-specific sentiment lexicon and inferring sentiments at the segment level. Our framework can greatly reduce the human effort for building a domainspecific sentiment lexicon with high quality. Specifically, in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average FMeasure gains of 3%. Our sentiment classification model achieves approximately 1% greater accuracy than a state-of-the-art approach based on elementary discourse units.
REF proposed a semi-supervised framework for generating a domainspecific sentiment lexicon to reduce human effort for constructing a high quality domain-specific sentiment lexicon.
ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis
ACL
Computer Science