Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis

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 newdomain data, we proposed Adapted Na

• Type: Journal
• Year: 2009
• Where: ECIR
• Authors: Songbo Tan, Xueqi Cheng, Yuefen Wang, Hongbo Xu
• Keywords: ECIR 2009, Information Technology, Naive Bayes, Old-domain Data, Supervised Sentiment Classifier