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Convolutional Deep Belief Network Based Short Text Classification on Arabic Corpus

Abdelwahed Motwakel1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Radwa Marzouk4, Amira Sayed A. Aziz5, 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 Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Digital Media, 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, 45(3), 3097-3113. https://doi.org/10.32604/csse.2023.033945

Abstract

With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media. The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage. Besides, the DSOCDBN-STC model involves CDBN based classification model for Arabic short text. At last, the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method. To establish the enhanced performance of the DSOCDBN-STC model, a wide range of simulations have been performed. The simulation results confirmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.

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APA Style
Motwakel, A., Al-onazi, B.B., Alzahrani, J.S., Marzouk, R., Aziz, A.S.A. et al. (2023). Convolutional deep belief network based short text classification on arabic corpus. Computer Systems Science and Engineering, 45(3), 3097-3113. https://doi.org/10.32604/csse.2023.033945
Vancouver Style
Motwakel A, Al-onazi BB, Alzahrani JS, Marzouk R, Aziz ASA, Zamani AS, et al. Convolutional deep belief network based short text classification on arabic corpus. Comput Syst Sci Eng. 2023;45(3):3097-3113 https://doi.org/10.32604/csse.2023.033945
IEEE Style
A. Motwakel et al., "Convolutional Deep Belief Network Based Short Text Classification on Arabic Corpus," Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 3097-3113. 2023. https://doi.org/10.32604/csse.2023.033945



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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