Paper: Mine the Easy Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification

ACL ID P09-1079
Title Mine the Easy Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive pro- cess of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part to the prevalence of sentimentally ambiguous re- views, where reviewers discuss both the positive and negative aspects of a prod- uct. To address this problem, we pro- pose a semi-supervised approach to senti- ment classification where we first mine the unambiguous reviews using spectral tech- niques and then exploit them to classify the ambiguous reviews via a novel com- bination of active learning, transductive learning, an...