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

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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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Publication date2011
Publication titleProceeding : HLT '11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies
EditorsDekang Lin
Place of PublicationStroudsburg, PA (US)
PublisherAssociation for Computational Linguistics
Number of pages9
ISBN (Print)978-1-932432-87-9
Original languageEnglish
Event49th annual meeting of the association for computational linguistics, HLT '11 - Portland, OR, United States
Duration: 19 Jun 201124 Jun 2011


Conference49th annual meeting of the association for computational linguistics, HLT '11
CountryUnited States
CityPortland, OR

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