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Author Shojaee, S. ♦ Murad, M.A.A. ♦ Bin Azman, A. ♦ Sharef, N.M. ♦ Nadali, S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Syntactic ♦ Opinion ♦ Deceptive ♦ Companies ♦ Classification ♦ Syntactics ♦ Lexical
Abstract 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.
Description Author affiliation: Fac. of Comput. Sci. & Inf. Technol., Univ. Putra Malaysia, Serdang, Malaysia (Shojaee, S.; Murad, M.A.A.; Bin Azman, A.; Sharef, N.M.; Nadali, S.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-12-08
Publisher Place Malaysia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479935161
Size (in Bytes) 314.16 kB
Page Count 6
Starting Page 53
Ending Page 58


Source: IEEE Xplore Digital Library