Deceptive Opinions Detection Using New Proposed Arabic Semantic Features - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Procedia Computer Science Année : 2021

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Résumé

Some users try to post false reviews to promote or to devalue other's products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to calculate the proposed features. These last were used basically to represent the reviews texts in the classification phase. Thus, the most accurate classification technique used in this area which has been proven by several previous works is the Support Vector Machine classifier (SVM). But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area. Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.
Fichier principal
Vignette du fichier
1-s2.0-S1877050921011601-main - copie.pdf (1000.6 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03299022 , version 1 (25-07-2021)

Identifiants

Citer

Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, et al.. Deceptive Opinions Detection Using New Proposed Arabic Semantic Features. Procedia Computer Science, 2021, 189, pp.29 - 36. ⟨10.1016/j.procs.2021.05.067⟩. ⟨hal-03299022⟩
62 Consultations
72 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More