Arabic sentiment analysis of YouTube comments

AL TAMIMI, Abdel-Karim, SHATNAWI, Ali and BANI-ISSA, Esraa (2018). Arabic sentiment analysis of YouTube comments. In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). Piscataway, New Jersey, IEEE, 1-6.

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Abstract

With the current level of ubiquity of social media websites, obtaining the users preferences automatically became a crucial task to assess their tendencies and behaviors online. Arabic language as one of the most spoken languages in the world and the fastest growing language on the Internet motivates us to provide reliable automated tools that can perform sentiment analysis to reveal users opinions. In this paper, we present our work of Arabic comments classification based on our collected and manually annotated YouTube Arabic comments. We share our classification results utilizing the most commonly used supervised classifiers: SVM-RBF, KNN, and Bernoulli NB classifiers. Experiments were performed using both raw and language-normalized datasets. We show that SVM-RBF outperformed other classification methods with an f-measure of 88.8% using a normalized dataset with two polarities.

Item Type:	Book Section
Additional Information:	2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 11-13 October 2017, Aqaba, Jordan. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Uncontrolled Keywords:	Natural Language Processing; Arabic Sentiment Analysis; Opinion Mining; Classification Algorithms
Identification Number:	https://doi.org/10.1109/aeect.2017.8257766
Page Range:	1-6
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	26 Apr 2023 16:47
Last Modified:	11 Oct 2023 15:45
URI:	https://shura.shu.ac.uk/id/eprint/31050

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