Music Recommendation System Based on Emotion

Abstract

With the onset of lockdown in the COVID-19 scenario, people were forced to confine themselves within the four walls of their rooms which in the meantime invited mood disorders like depression, anxiety etc. Music has proven to be a potential empathetic companion in this tough time for all. The proposed emotion-based music recommendation system uses user emotion as an input to recommend songs that are ascertained using facial expression or using direct inputs from the user. The model uses a Random Forest classifier and XGBoost algorithm to identify the song's emotion considering various features like instrumentalness, energy, acoustics, liveness, etc. and lyrical similarity among songs with the help of Term-Frequency times Inverse Document-Frequency (TF-IDF). The results of comprehensive experiments on real data confirm the accuracy of the proposed emotion classification system that can be integrated into any recommendation engine.