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Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

Abdelwahed Motwakel1,*, Najm Alotaibi2, Eatedal Alabdulkreem3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Mohamed K Nour5, Radwa Marzouk6, Mahmoud Othman7

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Prince Saud AlFaisal Institute for Diplomatic Studies, Riyadh, Saudi Arabia
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
5 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
6 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
7 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, 47(1), 1249-1266. https://doi.org/10.32604/csse.2023.034227

Abstract

Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural Network (CNN) model and conducted SA for Hindi movie reviews. The current study introduces an Effective Improved Metaheuristics with Deep Learning (DL)-Enabled Sentiment Analysis for Movie Reviews (IMDLSA-MR) model. The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format. Besides, the Term Frequency-Inverse Document Frequency (TF-IDF) model is exploited to generate the word vectors from the pre-processed data. The Deep Belief Network (DBN) model is utilized to analyse and classify the sentiments. Finally, the improved Jellyfish Search Optimization (IJSO) algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model, which shows the novelty of the work. Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model. The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.

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Cite This Article

APA Style
Motwakel, A., Alotaibi, N., Alabdulkreem, E., Alshahrani, H., Elfaki, M.A. et al. (2023). Improved metaheuristics with deep learning enabled movie review sentiment analysis. Computer Systems Science and Engineering, 47(1), 1249-1266. https://doi.org/10.32604/csse.2023.034227
Vancouver Style
Motwakel A, Alotaibi N, Alabdulkreem E, Alshahrani H, Elfaki MA, Nour MK, et al. Improved metaheuristics with deep learning enabled movie review sentiment analysis. Comput Syst Sci Eng. 2023;47(1):1249-1266 https://doi.org/10.32604/csse.2023.034227
IEEE Style
A. Motwakel et al., "Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis," Comput. Syst. Sci. Eng., vol. 47, no. 1, pp. 1249-1266. 2023. https://doi.org/10.32604/csse.2023.034227



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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