Music plays a significant role in influencing and reflecting human emotions. Traditional music recommendation systems, however, often fail to consider the listener's emotional state, leading to less personalized user experiences. An emotion-based music recommendation system that leverages artificial intelligence (AI) and machine learning (ML) techniques to identify and respond to user emotions. The system utilizes facial expression analysis and natural language processing to detect emotions in real-time. A recommendation algorithm then matches these emotions with appropriate music tracks, drawing from a diverse music database. Experimental results demonstrate that the emotion-based recommendation system significantly improves the accuracy of recommendations and user satisfaction compared to standard recommendation methods. The findings suggest that incorporating emotional context into music recommendation systems can enhance personalization and user engagement. Future research directions include expanding the system's emotion detection capabilities through multi-modal input and exploring real-time user feedback for dynamic adjustments.The project will commence with data collection from various sources, including APIs from platforms like Spotify and Genius, to gather song metadata, lyrics, and audio characteristics. We will employ advanced NLP techniques to analyze sentiment and categorize songs into emotions such as happiness, sadness, energy, calmness, and anger.
Emotion recognition, music recommendation, AI, machine learning, facial expression analysis, NLP, personalization, sentiment analysis, real-time detection
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