Opinion classification with tree kernel SVM using linguistic modality analysis

Takeshi S. Kobayakawa, Tadashi Kumano, Hideki Tanaka, Naoaki Okazaki, Jin Dong Kim, Jun'ichi Tsujii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We propose a method for classifying opinions which captures the role of linguistic modalities in the sentence. We use features than simple bag-of-words or opinion-holding predicates. The method is based on a machine learning and utilizes opinion-holding predicates and linguistic modalities as features. Two different detectors help to classify the opinions: the opinion-holding predicate detector and the modality detector. An opinion in the target is first parsed into a dependency structure, and then the opinion-holding predicates and modalities stick onto the leaf nodes of the dependency tree. The whole tree is regarded as input features of the opinion, and it becomes the input of tree kernel support vector machines. We have applied method to opinions in Japanese about television programs, and have confirmed the effectiveness of the method against conventional bag-of-words features, or against simple opinion-holding predicates features.

Original languageEnglish
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1791-1794
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2009 Nov 22009 Nov 6

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Country/TerritoryChina
CityHong Kong
Period09/11/209/11/6

Keywords

  • Knowledge management
  • Large-scale statistical techniques
  • Sentiment analysis
  • Text mining

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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