AI-Driven Emotion Analytics For Emergency Management in Tourism Using Improved CNN

Emotion recognition plays a critical role in enhancing human-computer interactions, particularly in dynamic environments like the tourism industry. During emergency events, understanding tourists' emotions can aid in decision-making, safety measures, and overall experience management. This study leverages deep learning methodologies, particularly Convolutional Neural Networks (CNN), to classify and analyze emotional states. The proposed system integrates image preprocessing, feature extraction, and advanced classification techniques to improve accuracy and efficiency. By incorporating real-time emotion detection, the model enhances responsive management strategies, ensuring improved safety and customer satisfaction