A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations
Received: 13 August 2023 | Revised: 24 August 2023 | Accepted: 26 August 2023 | Online: 4 September 2023
Corresponding author: Asif Nawaz
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.
Keywords:sentiment analysis, reviews, classification, deep learning, recommendations
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Copyright (c) 2023 Muhammad Rizwan Rashid Rana, Asif Nawaz, Tariq Ali, Ahmed M. El-Sherbeeny, Waqar Ali
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