CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews

  • Shakeel Ahmad Department of Computer Science, Faculty of Computing, and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
  • Sheikh Muhammad Saqib Department of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan
  • Asif Hassan Syed Department of Computer Science, Faculty of Computing, and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia

Abstract

Companies with diverse product offerings rely on customer reviews to gauge product reception. Following a purchase, customers often share their opinions on the website. Prospective buyers, prior to deciding, typically peruse these reviews to inform their choices. Analysing such feedback, whether positive or negative, holds paramount importance for companies seeking to improve product quality. Researchers are actively exploring methods to categorize comments based on sentiment scores. Notably, customers may express their reviews in Arabic text. Despite challenges such as the structure and morphology of Arabic text, a scarcity of machine-readable Arabic dictionaries, and limited tools for handling Arabic text, minimal progress has been made in the analysis of Arabic reviews. While some attempts have been undertaken, they have achieved suboptimal accuracy. In response, the authors propose a hybrid deep learning model comprising a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with GlobalMaxPooling. Through multiple iterations, the authors fine-tuned the proposed model and applied it to the publicly available Arabic Reviews dataset, achieving a notable 95% accuracy, precision, recall, and F1 score. The results indicate that, when compared to alternative models, the proposed model exhibits superior accuracy.

Published
Apr 7, 2024
How to Cite
AHMAD, Shakeel; SAQIB, Sheikh Muhammad; SYED, Asif Hassan. CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 43, n. 2, p. 183-194, apr. 2024. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/3130>. Date accessed: 24 may 2024. doi: http://dx.doi.org/10.22581/muet1982.3130.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License