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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Heba Mohsen4, Mohamed I. Eldesouki5, Mohammed Rizwanullah1

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Applied Linguistics, College of Languages, 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, Makkah 24211, Saudi Arabia
4 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
5 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2619-2635. https://doi.org/10.32604/csse.2023.034519

Abstract

Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools. These sources were used for processing and analyzing the Arabic language content like lexicons. However, an important challenge in this domain is the unavailability of sufficient and reliable resources. In this background, the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification (BROFDL-AASC) technique. The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language. In the presented BROFDL-AASC model, data pre-processing is performed at first to convert the input data into a useful format. Besides, the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine (DFRBM) model for the identification and categorization of sentiments. Furthermore, the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model. This scenario establishes the novelty of current study. The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

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APA Style
Hamza, M.A., Alshahrani, H.J., Alzahrani, J.S., Mohsen, H., Eldesouki, M.I. et al. (2023). Battle royale optimization with fuzzy deep learning for arabic sentiment classification. Computer Systems Science and Engineering, 46(2), 2619-2635. https://doi.org/10.32604/csse.2023.034519
Vancouver Style
Hamza MA, Alshahrani HJ, Alzahrani JS, Mohsen H, Eldesouki MI, Rizwanullah M. Battle royale optimization with fuzzy deep learning for arabic sentiment classification. Comput Syst Sci Eng. 2023;46(2):2619-2635 https://doi.org/10.32604/csse.2023.034519
IEEE Style
M.A. Hamza, H.J. Alshahrani, J.S. Alzahrani, H. Mohsen, M.I. Eldesouki, and M. Rizwanullah "Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification," Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 2619-2635. 2023. https://doi.org/10.32604/csse.2023.034519



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