Predicting the top and bottom ranks of billboard songs using Machine Learning

TL;DR

This paper uses linguistic analysis and machine learning to predict whether songs will rank high or low on Billboard charts, achieving a precision of 0.76.

Contribution

It introduces a novel approach of analyzing lyrics with linguistic features and applying SVM to predict chart success.

Findings

Achieved 0.76 precision in classification

Linguistic features are effective predictors

SVM with radial kernel performs well

Abstract

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.