An ontology-based approach to sentiment classification of mixed opinions in online restaurant reviews

Hea Jin Kim, Min Song

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

2 Citations (Scopus)

Abstract

Consumers review other consumer's opinion and experience of the quality of various products before making purchase. Automatic sentiment analysis of WOM in the form of user product reviews, blog posts and comments in online forum can support strategies in areas such as search engines, recommender systems, and market research and benefit to both consumers and sellers. The ontology-based approach designed in this work aims to investigate how to detect and classify mixed positive and negative opinions by interpreting with an ontology containing opinion information on terms. Our research question is whether disinterested subjectivity scores of sentiment ontology are pertinent to sentiment orientations not affected by reviewer's linguistic bias. The experimental results adopting opinion lexical resource achieve better and more stable performance in F-measure.

Original languageEnglish
Title of host publicationSocial Informatics - 5th International Conference, SocInfo 2013, Proceedings
Pages95-108
Number of pages14
DOIs
Publication statusPublished - 2013 Dec 1
Event5th International Conference on Social Informatics, SocInfo 2013 - Kyoto, Japan
Duration: 2013 Nov 252013 Nov 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8238 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Social Informatics, SocInfo 2013
CountryJapan
CityKyoto
Period13/11/2513/11/27

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, H. J., & Song, M. (2013). An ontology-based approach to sentiment classification of mixed opinions in online restaurant reviews. In Social Informatics - 5th International Conference, SocInfo 2013, Proceedings (pp. 95-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8238 LNCS). https://doi.org/10.1007/978-3-319-03260-3_9