Imbalanced sentiment classification

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

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

24 Citations (Scopus)

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 languageEnglish
Title of host publicationCIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Pages2469-2472
Number of pages4
DOIs
Publication statusPublished - 13 Dec 2011
Externally publishedYes
Event20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom
Duration: 24 Oct 201128 Oct 2011

Conference

Conference20th ACM Conference on Information and Knowledge Management, CIKM'11
CountryUnited Kingdom
CityGlasgow
Period24/10/1128/10/11

Keywords

  • imbalanced classification
  • opinion mining
  • sentiment classification

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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