Unraveling Emotions in Lyrics: A Novel Approach to Enhance Spotify Music Recommendations

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Chepkoech, Brenda
Issue Date
MSc in Artificial Intelligence
Dublin Business School
Music recommendation systems play a pivotal role in user engagement, satisfaction, and retention on streaming platforms like Spotify. However, traditional methods often fall short of providing diverse and emotionally resonant song suggestions, leading to repetitive playlists and user dissatisfaction. This research therefore delves into the unexplored area of emotion detection within song lyrics to enhance personalized music recommendations on Spotify. The study investigates various machine learning models, including Logistic Regression, Support Vector Machines, Bidirectional LSTM, and DistilBERT, to understand the intricate emotional hints within song lyrics. Through a comparative evaluation of these models, the research identifies Bidirectional LSTM as the most effective, achieving an accuracy of 92%, followed by Random Forest at 82%, and Support Vector Machines and Decision Tree both at 70%. Additionally, the research examines hybrid recommender systems combining case-based k Nearest Neighbours and content-based filtering to offer users nuanced and emotionally connecting song recommendations. The project seeks to optimize music discovery, boost user engagement, foster industry innovation, and ensure a more inclusive representation of artists and genres. Ultimately, the research aspires to introduce a novel perspective to music recommendation systems, one that authentically resonates with user emotions, preferences, and satisfaction.