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.
Original language | English |
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Title of host publication | CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management |
Pages | 2469-2472 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 13 Dec 2011 |
Externally published | Yes |
Event | 20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom Duration: 24 Oct 2011 → 28 Oct 2011 |
Conference
Conference | 20th ACM Conference on Information and Knowledge Management, CIKM'11 |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 24/10/11 → 28/10/11 |
Keywords
- imbalanced classification
- opinion mining
- sentiment classification
ASJC Scopus subject areas
- General Decision Sciences
- General Business,Management and Accounting