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

We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment classifiers. 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 inference 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 coarse-grained and fine-grained supervision benefits sentence-level sentiment analysis – an important task in the field of opinion classification and retrieval (Pang and Lee, 2008). Typical supervi...

• Type: Journal
• Year: 2011
• Where: ACL
• Authors: Oscar Täckström, Ryan T. McDonald
• Keywords: ACL 2011, Computational Linguistics, Inference Algorithms, Level Supervision, Sentiment Analysis