publication . Article . 2020

A Model for Predicting Music Popularity on Streaming Platforms

Carlos V.S. Araujo; Marco Cristo; Rafael Giusti;
Open Access
  • Published: 23 Dec 2020 Journal: Revista de Informática Teórica e Aplicada, volume 27, pages 108-117 (issn: 0103-4308, eissn: 2175-2745, Copyright policy)
  • Publisher: Universidade Federal do Rio Grande do Sul
Abstract
<jats:p>The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%.</jats:p>
Persistent Identifiers
Subjects
free text keywords: Computer Science, Music; Hit Song Science; Machine Learning; Spotify, Computer science, Machine learning, computer.software_genre, computer, Ranking, Radial basis function kernel, Svm classifier, Artificial intelligence, business.industry, business, Popularity, Popular music
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