Semi-supervised learning for imbalanced sentiment classification

Shoushan Li, Zhongqing Wang, Guodong Zhou, Yat Mei Lee

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

161 Citations (Scopus)


Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach.
Original languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Number of pages6
Publication statusPublished - 1 Dec 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: 16 Jul 201122 Jul 2011


Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CityBarcelona, Catalonia

ASJC Scopus subject areas

  • Artificial Intelligence


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