Semi-supervised Learning for Imbalanced Sentiment Classification
Shoushan Li, Zhongqing Wang and Guodong Zhou
Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sen-timent classification. However, most existing stud-ies assume the balance between negative and posi-tive 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 dynami-cally generated to deal with the imbalanced class distribution problem. Evaluation across four do-mains shows the effectiveness of our approach.