Automatically extracting polarity-bearing topics for cross-domain sentiment classification

He, Yulan; Lin, Chenghua and Alani, Harith (2011). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 19-24 Jun 2011, Portland, Oregon, USA.

URL: http://acl2011.org/accepted_papers.shtml

Abstract

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying 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. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Viewing alternatives

Look up in Google Scholar

Download history

Item Actions

Export

About

CORE (COnnecting REpositories)

Recommendations