A Comprehensive Review on Deep Learning Models for Customer Sentiment Analysis in E-commerce

Abstract

The explosive growth of e-commerce platforms has increased the necessity for precise and fast consumer sentiment research to understand user sentiments and improve company tactics. Due to their capacity to understand complex patterns from large volumes of data, deep learning models can automate sentiment analysis. This thorough research highlights the newest performance optimization methodologies for deep learning models for consumer sentiment analysis in e-commerce applications. We begin by discussing the importance of sentiment analysis in e-commerce and the problems of effectively gathering client thoughts. We next analyze RNNs, CNNs, and transformer-based models like BERT and its variations used for sentiment analysis. We also review current methods for improving deep learning models for e-commerce consumer sentiment research. These include data augmentation, transfer learning, attention mechanisms, ensemble methods, and domain adaptability. In addition, we present benchmark datasets and assessment criteria used to evaluate sentiment analysis models in e-commerce. We conclude by discussing domain-specific sentiment understanding, scalability, interpretability, and real-time analysis requirements for deep learning models for customer sentiment analysis in e-commerce, as well as current challenges and future research directions. This comprehensive review aims to inform e-commerce researchers, practitioners, and stakeholders about the latest methods for using deep learning models to analyze and understand customer sentiments, enabling informed decision-making and improving user experiences.