UPM Institutional Repository

Detecting deceptive reviews using lexical and syntactic features


Citation

Shojaee, Somayeh and Azmi Murad, Masrah Azrifah and Azman, Azreen and Mohd Sharef, Nurfadhlina and Nadali, Samaneh (2013) Detecting deceptive reviews using lexical and syntactic features. In: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA), 8-10 Dec. 2013, Bangi, Selangor, Malaysia. (pp. 53-58).

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.


Download File

Full text not available from this repository.

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/ISDA.2013.6920707
Publisher: IEEE (IEEEXplore)
Keywords: Deceptive; Opinion; Lexical; Syntactic; Classification
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 04 Nov 2015 02:02
Last Modified: 04 Nov 2015 02:02
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ISDA.2013.6920707
URI: http://psasir.upm.edu.my/id/eprint/41326
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item