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Music plays a vital role in influencing emotions, mood, and mental health. However, classical music recommendation systems mostly rely on listening to history, genre preference, or popularity, ignoring the listener’s mood. Motivation to go further in this area gave birth to this study, where a new sentiment-based music recommendation system is being designed by incorporating Natural Language Processing (NLP) and Machine Learning (ML) techniques to provide emotion-aware song recommendations. The system collects various audio features such as valence, energy, tempo, and danceability from music distribution platforms such as Spotify, which are well-known indicators for classifying the emotional tone of a song. Thereafter, NLP processes are used to analyse audio features and provide sentiment scores assigned to each music track: positive, negative, or neutral. These sentiment scores were further used with other song features, such as genre and tempo, to build in-depth emotional profiles for each song. Three ML methods were implemented in the system for classification and recommendation: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). After many trials, the SVM scored the highest in sentiment classification accuracy (87.5%), with maximum precision and recall values of 0.88. The recommendation is fed through a simple interface on a website where the user can enter their feelings and obtain song recommendations instantly determined by mood. According to the survey, 78% of users said that mood-based recommendations fit their emotional state better than traditional recommendations. Although the results prove this, limitations have been noted, particularly with a limited range of features and small dataset. Future enhancements will focus on real-time affect tracking, additional affect features, and larger and more diverse datasets. Traditional NLP applies to text data, but this system applies sentiment detection to numerical audio features. This version does not use lyric-based NLP.
Kumaresan et al. (Sat,) studied this question.