Proceedings

Semi-Supervised Learning for Imbalanced Sentiment Classification

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1826-1830.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-306

Abstract

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.

Shoushan Li, Zhongqing Wang, Guodong Zhou, and Sophia Yat Mei Lee. 2011. Semi-Supervised Learning for Imbalanced Sentiment Classification. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1826–1830. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-306
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