Sentiment classification considering negation and contrast transition

Shoushan Li, Chu-ren Huang

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

19 Citations (Scopus)


Negation and contrast transition are two kinds of linguistic phenomena which are popularly used to reverse the sentiment polarity of some words and sentences. In this paper, we propose an approach to incorporate their classification information into our sentiment classification system: First, we classify sentences into sentiment reversed and non-reversed parts. Then, represent them as two different bags-of-words. Third, present three general strategies to do classification with two-bag-of-words modeling. We collect a large-scale product reviews involving five domains and conduct our experiments on them. The experimental results show that incorporating both negation and contrast transition information is effective and performs robustly better than traditional machine learning approach (based on one-bag-of-words modeling) across five different domains.
Original languageEnglish
Title of host publicationPACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation
Number of pages10
Publication statusPublished - 1 Dec 2009
Event23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23 - Hong Kong, Hong Kong
Duration: 3 Dec 20095 Dec 2009


Conference23rd Pacific Asia Conference on Language, Information and Computation, PACLIC 23
Country/TerritoryHong Kong
CityHong Kong


  • Linear classifier
  • Opinion mining
  • Sentiment classification

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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