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.
|Title of host publication||Proceeding : HLT '11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies|
|Place of Publication||Stroudsburg, PA (US)|
|Publisher||Association for Computational Linguistics|
|Number of pages||9|
|Publication status||Published - 2011|
|Event||49th annual meeting of the association for computational linguistics, HLT '11 - Portland, OR, United States|
Duration: 19 Jun 2011 → 24 Jun 2011
|Conference||49th annual meeting of the association for computational linguistics, HLT '11|
|Period||19/06/11 → 24/06/11|
He, Y., Alani, H., & Lin, C. (2011). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. In D. Lin (Ed.), Proceeding : HLT '11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies (Vol. 1, pp. 123-131). Association for Computational Linguistics.