Emotion-Based Music Recommendation System Using Deep Learning

Emotion-aware music recommendation systems have emerged as a promising approach to enhancing user experiences by tailoring music suggestions to emotional states. This paper presents a novel system that integrates facial emotion recognition and music recommendation, leveraging state-of-the-art deep learning and natural language processing techniques. Emotion recognition is achieved using fine-tuned ResNet50V2 and VGG16 models, which classify facial expressions with improved accuracy. For music recommendation, the system employs BERT to analyze song lyrics and metadata, enabling dy-namic alignment between user emotions and musical attributes such as tempo, mood, and lyrical themes. Experimental results demonstrate an emotion recognition accuracy and high user satisfaction in music recommendations. The system offers real-time contextual adaptation, ensuring seamless and personalized music experiences. Future directions include multimodal emotion detection and incorporating user contexts like location and activity for further personalization. This work highlights the potential of emotion-aware systems in revolutionizing music recommendation technologies,