Document Type

Article

Source of Publication

Procedia Computer Science

Publication Date

1-1-2021

Abstract

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.

ISSN

1877-0509

Publisher

Elsevier

Volume

189

First Page

29

Last Page

36

Disciplines

Computer Sciences

Keywords

Deceptive Opinions Detection, Opinion Mining, Arabic Language, Semantic Features, Support Vector Machine, Semi Supervised Learning

Scopus ID

85112421998

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

Download

Share

COinS