MusicMood: Predicting the mood of music from song lyrics using machine learning
This work addresses sentiment prediction for music recommendation in public settings like hospitals or restaurants, but it is incremental as it applies an existing method to a specific domain.
The paper tackled predicting music mood from song lyrics using a naive Bayes classifier, achieving high precision in detecting happy moods based on text features.
Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.