Emotion Detection for Customer Satisfaction Using Optimized XLNet Model

A robust mechanism is developed to accurately assess customer emotion. The system analyses text content and extracts emotional characteristics using the XLNet model. This approach makes it possible to better understand customer feedback through the identification of small contextual indications and sentiment patterns that range from positive to negative emotions. The enhanced ability of the model to understand contextual links and bidirectional relationships in text improves the system's performance on sentiment classification tasks. In customer-focused industries, the solution is designed to provide valuable information that may improve decision-making, service delivery, and customer pleasure. The system ensures scalability, real-time processing, and adaptation across a variety of industries, such as e-commerce, healthcare, and entertainment.