Classification of Smartphone Product Reviews on E-Commerce using the Recurrent Neural Network (RNN) Method
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Abstract
Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might be overrepresented while others are missing. In this study, we tackled these challenges by collecting a large number of smartphone reviews from a leading e-commerce site. We used a Recurrent Neural Network (RNN) with a bidirectional Long Short-Term Memory (LSTM) architecture, which is good at understanding the context and meaning in sequences of words. Our approach also involved optimizing the model with the Adam optimizer, using 100-dimensional word embeddings, and applying dropout regularisation to prevent overfitting. For comparison, we tested more traditional techniques, like Support Vector Machine (SVM) and Naïve Bayes, against our RNN model. By balancing the dataset with random oversampling, the RNN achieved an impressive accuracy of 95.13%, outperforming the baseline methods by 7–9%. Overall, our results highlight the potential of advanced neural network models in improving sentiment analysis for e-commerce platforms, especially in challenging environments. This research provides a practical foundation for future work in natural language processing and can help businesses better understand and respond to their customers’ needs.
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