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.



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.

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