Sentiment classification : The contribution of ensemble learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

134 Scopus Citations
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Author(s)

  • Gang Wang
  • Jianshan Sun
  • Jian Ma
  • Kaiquan Xu
  • Jibao Gu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)77-93
Journal / PublicationDecision Support Systems
Volume57
Issue number1
Early online date15 Aug 2013
Publication statusPublished - Jan 2014

Abstract

With the rapid development of information technologies, user-generated contents can be conveniently posted online. While individuals, businesses, and governments are interested in evaluating the sentiments behind this content, there are no consistent conclusions on which sentiment classification technologies are best. Recent studies suggest that ensemble learning methods may have potential applicability in sentiment classification. In this study, we conduct a comparative assessment of the performance of three popular ensemble methods (Bagging, Boosting, and Random Subspace) based on five base learners (Naive Bayes, Maximum Entropy, Decision Tree, K Nearest Neighbor, and Support Vector Machine) for sentiment classification. Moreover, ten public sentiment analysis datasets were investigated to verify the effectiveness of ensemble learning for sentiment analysis. Based on a total of 1200 comparative group experiments, empirical results reveal that ensemble methods substantially improve the performance of individual base learners for sentiment classification. Among the three ensemble methods, Random Subspace has the better comparative results, although it was seldom discussed in the literature. These results illustrate that ensemble learning methods can be used as a viable method for sentiment classification. © 2013 Elsevier B.V.

Research Area(s)

  • Bagging, Boosting, Ensemble learning, Random Subspace, Sentiment classification

Citation Format(s)

Sentiment classification : The contribution of ensemble learning. / Wang, Gang; Sun, Jianshan; Ma, Jian; Xu, Kaiquan; Gu, Jibao.

In: Decision Support Systems, Vol. 57, No. 1, 01.2014, p. 77-93.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review