Paper: Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification

ACL ID P11-1013
Title Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Joint sentiment-topic (JST) model was previ- ously proposed to detect sentiment and topic simultaneously from text. The only super- vision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by in- corporating word polarity priors through mod- ifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Further- more, using feature augmentation and selec- tion according to the information gain criter...