Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture

TL;DR

This paper explores using DistilBERT and BERT embeddings to classify music genres, predict success, and estimate release years from song lyrics, achieving promising accuracy and insights into emotional content understanding.

Contribution

It introduces an NLP approach combining DistilBERT and SVMs for multi-faceted song lyric analysis, improving genre and success prediction accuracy.

Findings

65% genre classification accuracy

79% success prediction accuracy

SVM achieved RMSE of 14.18 for release year prediction

Abstract

This paper presents a natural language processing (NLP) approach to the problem of thoroughly comprehending song lyrics, with particular attention on genre classification, view-based success prediction, and approximate release year. Our tests provide promising results with 65\% accuracy in genre classification and 79\% accuracy in success prediction, leveraging a DistilBERT model for genre classification and BERT embeddings for release year prediction. Support Vector Machines outperformed other models in predicting the release year, achieving the lowest root mean squared error (RMSE) of 14.18. Our study offers insights that have the potential to revolutionize our relationship with music by addressing the shortcomings of current approaches in properly understanding the emotional intricacies of song lyrics.