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Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Abdelwahed Motwakel1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Sana Alazwari4, Mahmoud Othman5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Amgad Atta Abdelmageed1

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Abdelwahed Motwakel. Email: email

Computer Systems Science and Engineering 2023, 46(3), 3321-3338. https://doi.org/10.32604/csse.2023.033901

Abstract

Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a three-stage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attention-based cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

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APA Style
Motwakel, A., Al-onazi, B.B., Alzahrani, J.S., Alazwari, S., Othman, M. et al. (2023). Improved ant lion optimizer with deep learning driven arabic hate speech detection. Computer Systems Science and Engineering, 46(3), 3321-3338. https://doi.org/10.32604/csse.2023.033901
Vancouver Style
Motwakel A, Al-onazi BB, Alzahrani JS, Alazwari S, Othman M, Zamani AS, et al. Improved ant lion optimizer with deep learning driven arabic hate speech detection. Comput Syst Sci Eng. 2023;46(3):3321-3338 https://doi.org/10.32604/csse.2023.033901
IEEE Style
A. Motwakel et al., "Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 3321-3338. 2023. https://doi.org/10.32604/csse.2023.033901



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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