Deep semantic frame-based deceptive opinion spam analysis

Seongsoon Kim, Hyeokyoon Chang, Seongwoon Lee, Minhwan Yu, Jaewoo Kang

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

20 Citations (Scopus)

Abstract

User-generated content is becoming increasingly valuable to both individuals and businesses due to its usefulness and influence in e-commerce markets. As consumers rely more on such information, posting deceptive opinions, which can be deliberately used for potential profit, is becoming more of an issue. Existing work on opinion spam detection focuses mainly on linguistic features such as n-grams, syntactic patterns, or LIWC. However, deep semantic analysis remains largely unstudied. In this paper, we propose a frame-based deep semantic analysis method for understanding rich characteristics of deceptive and truthful opinions written by various types of individuals including crowdsourcing workers, employees who have expert-level domain knowledge about local businesses, and online users who post on Yelp and Tri-pAdvisor. Using our proposed semantic frame feature, we developed a classification model that outperforms the baseline model and achieves an accuracy of nearly 91%. Also, we performed qualitative analysis of deceptive and truthful review datasets and considered their semantic differences. Finally, we successfully found some interesting features that existing methods were unable to identify.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
PublisherAssociation for Computing Machinery
Pages1131-1140
Number of pages10
Volume19-23-Oct-2015
ISBN (Print)9781450337946
DOIs
Publication statusPublished - 2015 Oct 17
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: 2015 Oct 192015 Oct 23

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period15/10/1915/10/23

Keywords

  • Deceptive opinion spam
  • FrameNet
  • Semantic analysis

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Deep semantic frame-based deceptive opinion spam analysis'. Together they form a unique fingerprint.

  • Cite this

    Kim, S., Chang, H., Lee, S., Yu, M., & Kang, J. (2015). Deep semantic frame-based deceptive opinion spam analysis. In International Conference on Information and Knowledge Management, Proceedings (Vol. 19-23-Oct-2015, pp. 1131-1140). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806551