Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
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Cross-domain classification is a challenging problem in the research of sentiment classification. In this study, we propose a novel approach to cross-domain sentiment classification by exploiting the classification knowledge from some emotion keywords. First, our approach uses some emotion keywords to extract the automatically-labeled samples with a high precision from the target area. Then, both the automatically-labeled samples from the target domain and the real labeled samples from the source domain are combined to be a new labeled data set. Third, all the labeled data and the unlabeled data in the target domain are used to perform cross-domain sentiment classification with a standard label-propagation algorithm. The empirical results demonstrate the effectiveness of our approach.