Predicting the top and bottom ranks of billboard songs using Machine Learning
Computation and Language
2015-12-07 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1512.01283,
title = {Predicting the top and bottom ranks of billboard songs using Machine Learning},
author = {Vivek Datla and Abhinav Vishnu},
journal= {arXiv preprint arXiv:1512.01283},
year = {2015}
}