Sentiment classification: The contribution of ensemble learning

  • Authors:

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

  • Affiliations:

  • School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, ...;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong and School of Management, University of Science and Technology of China, Hefei, Anhui, PR China;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Electronic Commerce, School of Business, Nanjing University, Nanjing, Jiangsu 210093, PR China;School of Management, University of Science and Technology of China, Hefei, Anhui, PR China

  • Venue:
  • Decision Support Systems
  • Year:
  • 2014

Quantified Score

Hi-index 0.00

Visualization

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.