Detecting deceptive reviews using lexical and syntactic features

Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylom...

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Bibliographic Details
Published inInternational Conference on Intelligent Systems Design and Applications pp. 53 - 58
Main Authors Shojaee, Somayeh, Murad, Masrah Azrifah Azmi, Bin Azman, Azreen, Sharef, Nurfadhlina Mohd, Nadali, Samaneh
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Classification
Companies
Deceptive
Lexical
Opinion
Syntactic
Syntactics
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Summary:Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylometric features, i.e. lexical and syntactic, using supervised machine learning classifiers, i.e. Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) and Naive Bayes, to detect deceptive opinion. Detecting deceptive opinion by a human reader is a difficult task because spammers try to write wise reviews, therefore it causes changes in writing style and verbal usage. Hence, considering the stylometric features help to distinguish the spammer writing style to find deceptive reviews. Experiments on an existing hotel review corpus suggest that using stylometric features is a promising approach for detecting deceptive opinions.
ISSN:2164-7143
DOI:10.1109/ISDA.2013.6920707