Paper: A Weakly Supervised Model for Sentence-Level Semantic Orientation Analysis with Multiple Experts

ACL ID D12-1014
Title A Weakly Supervised Model for Sentence-Level Semantic Orientation Analysis with Multiple Experts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

We propose the weakly supervised Multi- Experts Model (MEM) for analyzing the se- mantic orientation of opinions expressed in natural language reviews. In contrast to most prior work, MEM predicts both opinion po- larity and opinion strength at the level of in- dividual sentences; such fine-grained analysis helps to understand better why users like or dislike the entity under review. A key chal- lenge in this setting is that it is hard to ob- tain sentence-level training data for both po- larity and strength. For this reason, MEM is weakly supervised: It starts with potentially noisy indicators obtained from coarse-grained training data (i.e., document-level ratings), a small set of diverse base predictors, and, if available, small amounts of fine-grained train- ing data. We integrate thes...