Abstract
In the community of sentiment analysis, supervised learning techniques have been shown to perform very well. When transferred to another domain, however, a supervised sentiment classifier often performs extremely bad. This is so-called domain-transfer problem. In this work, we attempt to attack this problem by making the maximum use of both the old-domain data and the unlabeled new-domain data. To leverage knowledge from the old-domain data, we proposed an effective measure, i.e., Frequently Co-occurring Entropy (FCE), to pick out generalizable features that occur frequently in both domains and have similar occurring probability. To gain knowledge from the new-domain data, we proposed Adapted Naïve Bayes (ANB), a weighted transfer version of Naive Bayes Classifier. The experimental results indicate that proposed approach could improve the performance of base classifier dramatically, and even provide much better performance than the transfer-learning baseline, i.e. the Naïve Bayes Transfer Classifier (NTBC).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: CIKM (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: EMNLP 2002 (2002)
Aue, A., Gamon, M.: Customizing Sentiment Classifiers to New Domains: a Case Study. In: RANLP 2005 (2005)
Tan, S., Wu, G., Tang, H., Cheng, X.: A novel scheme for domain-transfer problem in the context of sentiment analysis. In: CIKM 2007 (2007)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Learning to classify text from labeled and unlabeled documents. In: AAAI 1998 (1998)
Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML (1999)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Zhang, H.: Chinese Lexical Analysis Using Hierarchical Hidden Markov Model. In: The Second SIGHAN workshop affiliated with 41st ACL (2003)
DaumeIII, H., Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26, 101–126 (2006)
Jiang, J., Zhai, C.: A Two-Stage Approach to Domain Adaptation for Statistical Classifiers. In: CIKM 2007 (2007)
Dai, W., Xue, G., Yang, Q., Yu, Y.: Transferring Naive Bayes Classifiers for Text Classification. In: AAAI 2007 (2007)
McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML Workshop on Learning for Text Categorization (1998)
Wilson, T., Wiebe, J., Hwa, R.: Recognizing Strong and Weak Opinion Clauses. Computational Intelligence 22(2), 73–99 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, S., Cheng, X., Wang, Y., Xu, H. (2009). Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-00958-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00957-0
Online ISBN: 978-3-642-00958-7
eBook Packages: Computer ScienceComputer Science (R0)