ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis

The sentiment captured in opinionated text provides interesting and valuable informa-tion for social media services. However, due to the complexity and diversity of linguistic representations, it is challeng-ing to build a framework that accurately extracts such sentiment. We propose a semi-supervised framework for generat-ing a domain-specific sentiment lexicon and inferring sentiments at the segment level. Our framework can greatly reduce the human effort for building a domain-specific sentiment lexicon with high qual-ity. Specifically, in our evaluation, work-ing with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average F-Measure gains of 3%. Our sentiment clas-sification model achieves approximately 1 % greater accuracy than a state-of-the-art approach based on elementary discourse units.