Paper: Semi-supervised latent variable models for sentence-level sentiment analysis

ACL ID P11-2100
Title Semi-supervised latent variable models for sentence-level sentiment analysis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural super- vision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment clas- sifiers. The proposed model is a fusion of a fully supervised structured conditional model and its partially supervised counterpart. This allows for highly efficient estimation and infer- ence algorithms with rich feature definitions. We describe the two variants as well as their component models and verify experimentally that both variants give significantly improved results for sentence-level sentiment analysis compared to all baselines. 1 Sentence-level sentiment analysis In this paper, we demonstrate how combining coa...