Thesis

In today’s digitally driven music landscape, understanding what drives a song’s popularity requires insight not only into its acoustic and lyrical content, but also into patterns of listener engagement across platforms.

This thesis explores the predictive and descriptive dimensions of song popularity by applying supervised and unsupervised machine learning models to a multi-source dataset integrating audio features, sentiment analysis, and temporal consumption behavior.

Drawing from a novel, multi-platform dataset that includes Billboard Hot 100 rankings, Spotify acoustic features and popularity scores, streaming, airplay, and sales metrics as reported on Luminate’s Music Connect, and lyrics from AZLyrics, the study investigates the relationships between musical structure, listener behavior, and popularity outcomes.