Source of Publication
Procedia Computer Science
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.
Deceptive Opinions Detection, Opinion Mining, Arabic Language, Semantic Features, Support Vector Machine, Semi Supervised Learning
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Ziani, Amel; Azizi, Nabiha; Schwab, Didier; Zenakhra, Djamel; Aldwairi, Monther; Chekkai, Nassira; Zemmal, Nawel; and Salah, Marwa Hadj, "Deceptive Opinions Detection Using New Proposed Arabic Semantic Features" (2021). All Works. 4340.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series