Imbalanced sentiment classification

Authors:
Shoushan Li;Guodong Zhou;Zhongqing Wang;Sophia Yat Mei Lee;Rangyang Wang
Affiliations:
Soochow University, Suzhou, China;Soochow University, Suzhou, China;Soochow University, Suzhou, China;Hong Kong Baptist University, Hong Kong, Hong Kong;Soochow University, Suzhou, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.