Emotion-driven music recommendations: Integrating CNN and KNN for personalized playlists

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Authors
Gikonyo Kamau, Nelson
Issue Date
2023-08
Degree
Masters in Artificial Intelligence
Publisher
Dublin Business School
Rights
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Abstract
This thesis delves into the captivating world of personalized music playlists, where emotions, technology, and user preferences converge. Through a unique blend of sentiment analysis, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN), we embark on a journey to create playlists that resonate with your feelings. The CNN deciphers emotions from facial expressions, shaping the emotional landscape, while KNN fine-tunes song recommendations for a harmonious experience. To ensure accuracy, a gradient boosting model steps in to validate emotional connections. We also explore the power of user feedback loops and the potential of multi-modal emotion recognition. With a strong ethical compass and an interdisciplinary approach, we uncover the profound connection between emotions and music recommendation. The results magnify the importance of emotions in shaping musical experiences, leading to a symphony of personalized playlists