Kang Liu
ABSTRACT
In this paper, we give out a two-stage approach for domain adaptation problem in sentiment classification. In the first stage, based on our observation that customers often use different words to comment on the similar topics in the different domains, we regard these common topics as the bridge to link the different domain-specific features. We propose a novel topic model named Transfer-PLSA to extract the topic knowledge between different domains. Through these common topics, the features in the source domain are corresponded to the target features, so that those domain-specific knowledge can be transferred across different domains. In the second step, we use the classifier trained on the labeled examples in the source domain to pick up some informative examples in the target domain. Then we retrain the classifier on these selected examples, so that the classifier is adapted for the target domain. Experimental results on sentiment classification in four different domains indicate that our method outperforms other traditional methods.
- John Blitzer, Mark Dredze and Fernando Pereira. 2007. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In Proc. of the 45th Annual Meeting of the Association of Computational Linguistics (ACL), pages 440--447.Google Scholar
- Hal Daumé III and Daniel Marcu. 2006. Domain Adaptation for Statistical Classifiers. Journal of Artificial Intelligence Research 26, pages 101--126. Google ScholarDigital Library
- Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang and Yong Yu. 2008. Translated Learning: Transfer Learning across different feature space. In Proc. of NIPS.Google Scholar
- T. Joachims, 1999, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press. Google ScholarDigital Library
- Thomas Hofmann. Probabilistic Latent Semantic Indexing.1999. In Proc. of SIGIR. Google ScholarDigital Library
- Songbo Tan, Gaowei Wu, Huifeng Tang and Zueqi Cheng. 2007. A Novel Scheme for Domain-transfer Problem in the context of Sentiment Analysis. In Proc. of CIKM 2007 Google ScholarDigital Library
- Gui-Rong Xue, Wenyuan Dai Qiang Yang and Yong Yu. 2008. Topic-bridged PLSA for Text Classification. In Proc. of SIGIR. Google ScholarDigital Library
Index Terms
- Cross-domain sentiment classification using a two-stage method
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