A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations


  • Muhammad Rizwan Rashid Rana University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Asif Nawaz University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Tariq Ali University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Ahmed M. El-Sherbeeny Industrial Engineering Department, College of Engineering, King Saud University, Saudi Arabia
  • Waqar Ali Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari university of Venice, Italy
Volume: 13 | Issue: 5 | Pages: 11739-11746 | October 2023 | https://doi.org/10.48084/etasr.6278


The advancement of technology has led to the rise of social media forums and e-commerce platforms, which have become popular means of communication, and people can express their opinions through comments and reviews. Increased accessibility to online feedback helps individuals make informed decisions about product purchases, services, and other decisions. This study used a sentiment analysis-based approach to improve the functionality of the recommendations from user reviews and consider the features (aspects and opinions) of products and services to understand the characteristics and attributes that influence the performance of classification algorithms. The proposed model consists of data preprocessing, word embedding, character representation creation, feature extraction using BiLSTM-CF, and classification using BiGRU. The proposed model was evaluated on different multidomain benchmark datasets demonstrating impressive performance. The proposed model outperformed existing models, offering more promising performance results in recommendations.


sentiment analysis, reviews, classification, deep learning, recommendations


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How to Cite

M. R. R. Rana, A. Nawaz, T. Ali, A. M. El-Sherbeeny, and W. Ali, “A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11739–11746, Oct. 2023.


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