Learning multilinguistic knowledge for opinion analysis

Ruifeng Xu, Kam Fai Wong, Qin Lu, Yunqing Xia

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

1 Citation (Scopus)


Most existing opinion analysis techniques used word-level sentiment knowledge but lack the learning capacity on the behaviors of context-dependent opinion words. Meanwhile, the use of collocation-level sentiment knowledge is not well studied. This paper presents an opinion analysis system, namely OA, which incorporates the word-level and collocation-level sentiment knowledge. Based on the observation on the NTCIR-6 opinion training corpus, some word-level and collocation-level linguistic clues for opinion analysis are discovered. Learning techniques are developed to learn the features corresponding to these discovered clues. These features are in turn incorporated into a classifier based on support vector machine to identify opinionated sentences and determine their polarities from running text. Evaluations on NTCIR-6 opinion testing dataset show that OA achieved promising overall performance.
Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Number of pages8
Publication statusPublished - 27 Nov 2008
Event4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China
Duration: 15 Sept 200818 Sept 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Intelligent Computing, ICIC 2008


  • Collocation
  • Linguistic Knowledge Learning
  • Opinion Analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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