Music recommendation system using natural language processing

Abstract

Music recommendation systems have become increasingly popular in recent years, facilitating personalized music discovery for users worldwide. This dissertation explores the application of natural language processing (NLP) and machine learning techniques in developing a music recommendation system. The study involves building a collection of music lyrics databases, analyzing the lyrics using NLP methods (such as TF-IDF and similarity/distance metrics), and integrating these findings into a recommendation model. The cosine similarity model was evaluated and recorded an accuracy of 96%, precision of 95%, recall of 96% and F1-score of 95%. Therefore, incorporating lyrics-based features in music recommendation systems can improve user experience in consuming recommendations of similar and relevant music.