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

ACL ID P14-1051
Title ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 ...