Open AccessOpen Access


Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

Hala J. Alshahrani1, Abdulbaset Gaddah2, Ehab S. Alnuzaili3, Mesfer Al Duhayyim4,*, Heba Mohsen5, Ishfaq Yaseen6, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6

1 Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
3 Department of English, College of Science & Art at Mahayil, King Khalid University, Muhayel Aseer, 63311, Saudi Arabia
4 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Aflaj, 16828, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, 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:

Intelligent Automation & Soft Computing 2023, 37(1), 283-300.


Sentiment analysis (SA) is a growing field at the intersection of computer science and computational linguistics that endeavors to automatically identify the sentiment presented in text. Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language. Sentiment is classified as a negative or positive assessment articulated through language. SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online (of a movie) can be negative or positive toward the thing that has been reviewed. Deep learning (DL) is becoming a powerful machine learning (ML) method for dealing with the increasing demand for precise SA. With this motivation, this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification (MSCADBN-MVC) technique. The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data. Primarily, the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process. For the classification of sentiments that exist in the movie reviews, the ADBN model is utilized in this work. At last, the hyperparameter tuning of the ADBN model is carried out using the MSCA technique, which integrates the Levy flight concepts into the standard sine cosine algorithm (SCA). In order to demonstrate the significant performance of the MSCADBN-MVC model, a wide-ranging experimental analysis is performed on three different datasets. The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.


Cite This Article

H. J. Alshahrani, A. Gaddah, E. S. Alnuzaili, M. A. Duhayyim, H. Mohsen et al., "Modified sine cosine optimization with adaptive deep belief network for movie review classification," Intelligent Automation & Soft Computing, vol. 37, no.1, pp. 283–300, 2023.

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.
  • 613


  • 397


  • 0


Share Link