Graph Neural Network for Smartphone Recommendation System: A Sentiment Analysis Approach for Smartphone Rating

• Published in: IEEE access 2023, Vol.11, p.140451-140463
• Main Authors: Ojo, Stephen, Abbas, Sidra, Marzougui, Mehrez, Sampedro, Gabriel Avelino, Almadhor, Ahmad S., Hejaili, Abdullah Al, Ivanochko, Iryna
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Biological system modeling; Cellular telephones; classification; Consumers; Data mining; Data models; Datasets; Decisions; Deep learning; Feature extraction; Flipkart smartphone rating; graph neural network; Graph neural networks; Machine learning; Mobile handsets; Neural networks; Predictive models; recommendation system; Recommender systems; Sentiment analysis; smartphone dataset; Smartphones; Social networking (online)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3341222

The increasing demand for mobile phones has resulted in abundant online reviews, making it challenging for consumers to make informed purchasing decisions. In this study, we propose Graph Neural Network (GNN) models to classify mobile phone ratings using Term Frequency-Inverse Document Frequency (TF-IDF) features. We collected a dataset of over 13,000 mobile phone evaluations from the Flipkart website. The proposed method includes data purification, balancing, feature extraction from the TF-IDF, and model prediction using deep learning models. The proposed approach utilized other models such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM to compare other classifiers. The experiments' outcomes demonstrate that the suggested model performs better than conventional deep learning methods regarding accuracy and efficiency. The GNN model achieved the best 99.0% accuracy rate. The proposed approach can help consumers make informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.