Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
A weighted profile intersection measure for profile-based authorship attribution
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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We address in this paper the adaptation problem in sentiment classification. As we know, available labeled data required by sentiment classifiers does not always exist. Given a set of labeled data from different domains and a collection of unlabeled data of the target domain, it would be interesting to determine which subset of those domains has a feature distribution similar to the target domain. In this way, in the absence of labeled data for a particular target domain, it would be plausible to make use of the labeled data corresponding to the most similar domains.