Making large-scale support vector machine learning practical
Advances in kernel methods
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
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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.