Machine Learning-Driven Emotional Feedback Analysis and Adaptive Content Generation for VR Movie and TV Users

With the growing demand for immersive audiovisual experiences, user sentiment feedback analysis has become a pivotal factor in improving personalization and interactivity in virtual reality (VR) movie and television. This study proposes a machine learning–driven framework that integrates sentiment feedback recognition and adaptive content generation to optimize user experience. First, a Long Short-Term Memory (LSTM) model is developed to analyze multimodal sentiment feedback data, including physiological signals, behavioral responses, and interactive actions. The model achieves an average recognition accuracy of 75.75% across four basic emotions—happiness, sadness, anger, and fear—demonstrating its ability to capture dynamic and continuous emotional patterns. Based on real-time sentiment feedback, a Deep Q-Network (DQN) reinforcement learning algorithm is employed to generate adaptive VR content that aligns with users’ current emotional states. Experimental validation with 100 participants shows that adaptive content generation increases overall satisfaction scores from 6.2 to 7.8, and the matching degree between user emotions and content improves by more than 20%. The integration of sentiment feedback analysis and reinforcement learning establishes a closed feedback loop—emotion detection → adaptive adjustment → feedback optimization—that enhances immersion, empathy, and user engagement. This research provides a data-driven reference for the intelligent evolution of VR movie and television, and future work will expand to fine-grained emotional dimensions and multimodal fusion to improve recognition precision and real-time adaptive generation performance.