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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Saud S. Alotaibi1, Eatedal Alabdulkreem2, Sami Althahabi3, Manar Ahmed Hamza4,*, Mohammed Rizwanullah4, Abu Sarwar Zamani4, Abdelwahed Motwakel4, Radwa Marzouk5

1 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca, 24382, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, 62529, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
5 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computer Systems Science and Engineering 2023, 45(1), 737-751. https://doi.org/10.32604/csse.2023.030170

Abstract

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

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Cite This Article

APA Style
Alotaibi, S.S., Alabdulkreem, E., Althahabi, S., Hamza, M.A., Rizwanullah, M. et al. (2023). Artificial fish swarm optimization with deep learning enabled opinion mining approach. Computer Systems Science and Engineering, 45(1), 737-751. https://doi.org/10.32604/csse.2023.030170
Vancouver Style
Alotaibi SS, Alabdulkreem E, Althahabi S, Hamza MA, Rizwanullah M, Zamani AS, et al. Artificial fish swarm optimization with deep learning enabled opinion mining approach. Comput Syst Sci Eng. 2023;45(1):737-751 https://doi.org/10.32604/csse.2023.030170
IEEE Style
S.S. Alotaibi et al., "Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach," Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 737-751. 2023. https://doi.org/10.32604/csse.2023.030170



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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