Exploring metal subgenres with Python
Today's post is about an exploratory analysis of metal band subgenres based on data scraped from the web. If you're too impatient and want to dive right in, choose one of the links below. If you like context, or are confused right now, read on. Fear not, you don't have to like metal music to appreciate the analysis.
The internet lets people share music from all over the world. I am a huge fan of metal music, as there is often high standard of musicianship. I have also been playing guitar for going on 15 years. I know a lot of the metal I listen to is made by bands from many different countries. I can think of metal bands I love from Sweden, France, Germany, Italy, Canada, Austrailia, the UK, and Norway just off the top of my head. While specific genres often grow out of a certain regional scene, there is no absolute rule that confines genres geographically. For instance, you don't have to be from Norway to start a black metal band.
Within the blanket of metal there are numerous subgenres that overlap, separate out, and merge with one another over time. Go to any metal forum on the internet and you're bound to find flame wars debating where certain bands fall within subgenres or where the boundary of one subgenre ends and another begins. The truth of it is that these categorizations are fuzzy and subjective. A single band can often traverse genres across subsequent albums or even within a single song. Or they might be the same throughout their entire career. Music is very personal as well, readily stoking the flames of debate. On top of it all, the words we use to describe metal subgenres can also be limiting compared to the information contained in a sound, a song, or an album. Despite all this, it is generally accepted that distinct subgenres do exist.
Luckily, there is the Metal Archives (M-A). An extraordinarily comprehensive user-contributed repository of information on metal bands. It has everything from band names with countries of origin, to timelines of lineup changes, to numerically scored album reviews. They also have a genre description for each band on their site. Just what we need to make definitive conclusions about genre memberships! As far as I know, the Metal Archives is the largest compendium of data on metal bands and I believe it is all user-contributed. That said, a huge thank you to the maintainers of the Metal Archives and all of the users who have contributed to it!
MA_scraper.py downloads only the information presented when you
browse bands alphabetically. This includes each band's name (with a link to the
band's M-A page), country of origin, genre, and status (active, split-up,
changed name, on hold, unknown, or disputed). The data was scraped on
2016-04-01 and consists of a little over 100,000 bands.
I started out just wanting to write a web scraper for the Metal Archives. After realizing that there was an incredibly huge amount of information up there, I decided to start with just basic band information. The most interesting of which to me was the genre description. Initial exploration of the data led me to three general questions:
- What groups of subgenres exist semantically?
- What words or terms define these groups?
- How are they all related?
The exploratory analysis I ended up with hits on these primary questions with a few interesting detours along the way. This took me through topics such as NLP, dimensionality reduction, clustering, basic graph theory, and of course data visualization.
What I found
Through exploring the data it became obvious that distinct groups or clusters of metal subgenres existed within the data set. These clusters are all based on semantic relationships between genre labels. What was surprising was the degree of structure within the data. There are some groups that overlap with neighboring groups, while there are other groups that are almost entirely isolated. This is a very exicting result given that the only underlying assumption I held was that subgenres exist (a fairly benign and obvious statement).
While I'm sure there are more interesting perspectives to be extracted from this particular data set, I decided to tie it off here but may come back to it in the future (predicting album review scores using NLP anyone?).
Is any of this useful? Well you could check out that section in the notebook and see for yourself!
A note on inspiration
A good amount of inspiration for the analysis I ended up doing came from reading Alex Kras' post where he analyzes comments from a single post by Mark Zuckerberg after trying to figure out how to be the first commenter on one of his posts. I really liked the graph visualization used and how he found the subset asking Zuckerberg for money. Oh and the word cloud was cool too!
Special thanks to Eric Ma for brainstorming analysis approaches & visualization techniques with me. Also thanks to Boston Python for holding great meetups where ideas for blog posts like this are born.