Mining Modern Music: The Classification of Popular Songs

Abstract

The rising popularity of streaming services has made in-depth musical data more accessible than ever before and has created new opportunities for data mining. This project utilizes data from 19,000 songs made available by Spotify. Several data mining algorithms (including J48 Decision Trees, Random Forest, Simple K Means, NaiveBayes, ZeroR, and JRIP) were used to assess the data as a classification task with the target class being popularity. The data was pre-processed and the popularity class was split into two different schemes, both of which were used to train the aforementioned algorithms with the goal of attaining the highest possible classification accuracy. Once reliable models were produced, the best performing algorithms were used in conjunction with association algorithms and Information Gain evaluation in order to assess the importance of features such as key, acousticness, tempo, instrumentalness, etc., in the prediction of the popularity class. Through this lens certain groups of attributes emerged as indicators of what makes a song popular or unpopular, and relationships between the attributes themselves were revealed as well. Overall it was concluded that popular music does in fact have patterns and a formulaic nature, making the “art” of creating music seem more like a science. However, within those patterns enough variation can be seen to account for different genres and musical moods that still persist in this era of pop music, and support the idea that as a modern musical community we still maintain some diversity.