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Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

Mesfer Al Duhayyim1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Abdullah Mohamed5, Ishfaq Yaseen6, Gouse Pasha Mohammed6, Mohammed Rizwanullah6, Abu Sarwar Zamani6

1 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, 16273, Saudi Arabia
2 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Makkah, 24211, Saudi Arabia
4 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
5 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email: email

Computer Systems Science and Engineering 2023, 47(3), 3049-3065. https://doi.org/10.32604/csse.2023.033836

Abstract

Sentiment analysis (SA) of the Arabic language becomes important despite scarce annotated corpora and confined sources. Arabic affect Analysis has become an active research zone nowadays. But still, the Arabic language lags behind adequate language sources for enabling the SA tasks. Thus, Arabic still faces challenges in natural language processing (NLP) tasks because of its structure complexities, history, and distinct cultures. It has gained lesser effort than the other languages. This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis (MVODRL-AA) on Arabic Corpus. The presented MVODRL-AA model majorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus. Firstly, the MVODRL-AA model follows data pre-processing and word embedding. Next, an n-gram model is utilized to generate word embeddings. A deep Q-learning network (DQLN) model is then exploited to identify and classify the effect on the Arabic corpus. At last, the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model, showing the novelty of the work. A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model. The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

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APA Style
Duhayyim, M.A., Al-onazi, B.B., Alzahrani, J.S., Alshahrani, H., Elfaki, M.A. et al. (2023). Multi-versus optimization with deep reinforcement learning enabled affect analysis on arabic corpus. Computer Systems Science and Engineering, 47(3), 3049-3065. https://doi.org/10.32604/csse.2023.033836
Vancouver Style
Duhayyim MA, Al-onazi BB, Alzahrani JS, Alshahrani H, Elfaki MA, Mohamed A, et al. Multi-versus optimization with deep reinforcement learning enabled affect analysis on arabic corpus. Comput Syst Sci Eng. 2023;47(3):3049-3065 https://doi.org/10.32604/csse.2023.033836
IEEE Style
M.A. Duhayyim et al., "Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 3049-3065. 2023. https://doi.org/10.32604/csse.2023.033836



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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