The Evolution of Pop Lyrics and a tale of two LDA’s

word cloud d3

Inspired by this amazing Paper, that used audio signalling processing to analyse 30 second clips, from around 17K pop songs, to understand the evolution of Pop music over the last 50 years. I thought it would be interesting to see if something similar could be done with Pop lyrics.

The Evolution of Pop paper explains how Latent Dirichlet Allocation (LDA) was used to describe musical topics in each song. These topics were based on the chord progressions, timbre, and harmonics in the song, as derived using audio signal processing techniques. The songs were then clustered together, according to these topics, to give 13 major genres of pop music. LDA is traditionally used in text analysis. I was interested to see if I could classify the same songs into the 13 major genres according to the lyrics in the songs.

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Spotify Related Artists Visualisation

Otis Shuggie Related Artists

This is my first post, so I needed some data to play with. I’ve been wanting to learn more about APIs so tackling the Spotify API seemed like a great place to start. I soon came across the related artists function in the API and that gave me a great idea. What if you could map out and visualise how your favourite artists relate to each other according to Spotify. It could be a useful way to discover new similar artists. A visual recommendation engine.

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