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Ensemble Learning for Sentiment Classification

  • Ying Su
  • Yong Zhang
  • Donghong Ji
  • Yibing Wang
  • Hongmiao Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7717)

Abstract

This paper presents an ensemble learning method for sentiment classification of reviews. The diversity among the machine learning algorithms for sentiment classification with different settings, which includes different features, different weight measures and the modeling of negation, is investigated in three domains, which gives a space for improving the performance. Then the ensemble learning framework, stacking generalization is introduced based on different algorithms with different settings, and compared with the majority voting. According to the characteristic of reviews, the opinion summary of review is proposed in this paper, which is composed of the first two and last two sentences of review. Results show that stacking has been proven to be consistently effective over all domains, working better than majority voting, and that using the opinion summary can improve the performance further.

Keywords

sentiment classification sentiment analysis stacked generalization diversity measure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ying Su
    • 1
  • Yong Zhang
    • 2
    • 3
  • Donghong Ji
    • 2
  • Yibing Wang
    • 4
  • Hongmiao Wu
    • 5
  1. 1.Department of Computer and ElectronicHuazhong University of Science and Technology Wuchang BranchWuhanP.R. China
  2. 2.Computer SchoolWuhan UniversityWuhanP.R. China
  3. 3.Department of Computer ScienceHuazhong Normal UniversityWuhanP.R. China
  4. 4.Third FacultySecond Artillery Command CollegeP.R. China
  5. 5.School of Foreign Languages and LiteratureWuhan UniversityWuhanP.R. China

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