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

Vivek Datla; Abhinav Vishnu

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arXiv · 1512.01283

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

Vivek Datla·Abhinav Vishnu

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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.

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Vivek Datla, Abhinav Vishnu. 2015-12-03. Predicting the top and bottom ranks of billboard songs using Machine Learning. https://arxiv.org/abs/1512.01283

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