A Deep Learning Approach to Emotion Recognition and Music Recommendation in Chat bots

Abstract

Music has become an integral part of modern life, significantly influencing human emotions, stress levels, and overall user experience. With the rapid expansion of online music streaming platforms and large-scale digital music libraries, users often face difficulties in selecting songs that align with their current emotional state and personal preferences. Conventional music recommendation systems primarily rely on listening history, collaborative filtering, or keyword-based techniques; however, these approaches frequently fail to capture users’ real-time emotions, resulting in less personalized recommendations. To address this limitation, this paper presents Music Mood, an intelligent multimodal music recommendation framework designed to deliver emotion-aware and personalized song recommendations. The proposed system integrates multiple emotion recognition modalities, including text sentiment analysis, speech emotion recognition, and facial expression detection, to accurately identify users’ emotional states. Text-based analysis extracts emotional cues from user interactions, speech processing identifies vocal characteristics and tone variations, and facial analysis captures visual emotional expressions. By combining these complementary modalities, the framework achieves more robust and accurate emotion detection compared to single-modal approaches. The identified emotional state is subsequently utilized to generate context-aware music recommendations tailored to individual user preferences. Experimental results demonstrate that the multimodal approach enhances recommendation accuracy, user satisfaction, and overall personalization, thereby providing a more engaging and adaptive music listening experience.