A novel scheme for domain-transfer problem in the context of sentiment analysis

In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing; I.2.7 [Artificial Intelligence]: Natural language processing General Terms Algorithms; Performance; Experimentation Keywords Sentiment Classification; Opinion Mining; Information Retrieval

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Songbo Tan, Gaowei Wu, Huifeng Tang, Xueqi Cheng
• Keywords: Base Classifier, Base Sentiment Classifier, CIKM 2007, Informative Unlabeled Examples