Multi-domain sentiment classification with classifier combination

Shou Shan Li, Chu-ren Huang, Cheng Qing Zong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach (using the training data individually) and mixed domain classification approach (simply combining all the training data). In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.
Original languageEnglish
Pages (from-to)25-33
Number of pages9
JournalJournal of Computer Science and Technology
Issue number1
Publication statusPublished - 1 Sep 2010


  • multi-domain learning
  • multiple classifier system
  • sentiment classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics

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