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

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 123–131, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Automatically Extracting Polarity-Bearing Topics for Cross-Domain Sentiment Classification Yulan He Chenghua Lin† Harith Alani Knowledge Media Institute, The Open University Milton Keynes MK7 6AA, UK {y.he,h.alani}@open.ac.uk † School of Engineering, Computing and Mathematics University of Exeter, Exeter EX4 4QF, UK cl322@exeter.ac.uk Abstract 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 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 tun- ing. 1 Introduction Given a piece of text, sentiment classification aims to determine whether the semantic orientation of the text is positive, negative or neutral. Machine learn- ing approaches to this problem (?; ?; ?; ?; ?; ?) typ- ically assume that classification models are trained and tested using data drawn from some fixed distri- bution. However, in many practical cases, we may have plentiful labeled examples in the source do- main, but very few or no labeled examples in the target domain with a different distribution. For ex- ample, we may have many labeled books reviews, but we are interested in detecting the polarity of electronics reviews. Reviews for different produces might have widely different vocabularies, thus clas- sifiers trained on one domain often fail to produce satisfactory results when shifting to another do- main. This has motivated much research on sen- timent transfer learning which transfers knowledge from a source task or domain to a different but re- lated task or domain (?; ?; ?; ?). Joint sentiment-topic (JST) model (?; ?) was ex- tended from the latent Dirichlet allocation (LDA) model (?) to detect sentiment and topic simultane- ously from text. The only supervision required by JST learning is domain-independent polarity word prior information. With prior polarity words ex- tracted from both the MPQA subjectivity lexicon1 and the appraisal lexicon2, the JST model achieves a sentiment classification accuracy of 74% on the movie review data3 and 71% on the multi-domain sentiment dataset4. Moreover, it is also able to ex- tract coherent and informative topics grouped under different sentiment. The fact that the JST model does not required any labeled documents for training makes it desirable for domain adaptation in senti- ment classification. Many existing approaches solve the …

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