Using deep linguistic features for finding deceptive opinion spam

Qiongkai Xu, Hai Zhao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


While most recent work has focused on instances of opinion spam which are manually identifiable or deceptive opinion spam which are written by paid writers separately, in this work we study both of these interesting topics and propose an effective framework which has good performance on both datasets. Based on the golden-standard opinion spam dataset, we propose a novel model which integrates some deep linguistic features derived from a syntactic dependency parsing tree to discriminate deceptive opinions from normal ones. On a background of multiple language tasks, our model is evaluated on both English (gold-standard) and Chinese (non-gold) datasets. The experimental results show that our model produces state-of-the-art results on both of the topics.
Original languageEnglish
Title of host publicationProceedings of COLING 2012
Subtitle of host publicationposters
EditorsMartin Kay, Christian Boitet
Place of PublicationPowai, India
PublisherThe COLING 2012 Organizing Committee
Number of pages10
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Computational Linguistics (24th : 2012) - Mumbai, India
Duration: 8 Dec 201215 Dec 2012


ConferenceInternational Conference on Computational Linguistics (24th : 2012)
CityMumbai, India


  • Opinion Spam
  • Multi-Language
  • Deep Linguistic Features


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