Predicting Music Popularity Using Music Charts

摘要

Online streaming platforms have become the most important method of music consumption. Most streaming platforms provide tools to assess the popularity of a song by means of scores and rankings. In this paper, we present a methodology for predicting if a song will appear on Spotify's Top 50 Global ranking after a certain amount of time. Spotify is one of the biggest streaming services and if a song is popular on this platform, it is likely to ensure good financial return to the artists and their label. We approach the problem as a classification task and employ classifiers built on past information from the platform's Top 50 Global ranking, in addition to acoustic features from the songs. The Support Vector Machine classifier with RBF kernel reached the best results in our experiments with an AUC higher than 80% when predicting the popularity of songs two months in advance.