Predicting the top and bottom ranks of billboard songs using Machine Learning
This work addresses the music industry's need to forecast song success, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of predicting whether a song reaches the top or bottom of Billboard charts by analyzing lyrics with computational linguistic algorithms, achieving a classification precision of 0.76 using an SVM classifier.
The music industry is a $130 billion industry. Predicting whether a song catches the pulse of the audience impacts the industry. In this paper we analyze language inside the lyrics of the songs using several computational linguistic algorithms and predict whether a song would make to the top or bottom of the billboard rankings based on the language features. We trained and tested an SVM classifier with a radial kernel function on the linguistic features. Results indicate that we can classify whether a song belongs to top and bottom of the billboard charts with a precision of 0.76.