A few years ago, I came across Matt Daniels’ study on rappers, ranked by the size of their vocabulary. The study regularly is updated throughout the years, to take into account of newer artists, or new albums artists have released. Being a fan of this visual essay, along with being a fan of modern rap, I wanted to center my praxis assignment as a smaller-scale version of the study: the top five rap albums of the past year, sorted by the size of their vocabulary.
I decided to visit Billboard to choose the top five albums. Although we are approaching the end of 2019, the franchise did not scale rap albums for the year. I looked to 2018’s top rap albums, which (in order) are Drake’s Scorpion, Post Malone’s beerbongs & bentleys, Cardi B’s Invasion of Privacy, Travis Scott’s Astroworld, and Post Malone’s other album, Stoney. To locate lyrics, I chose Genius. Unlike other lyric sites like Metrolyrics or AZlyrics, many artists directly contribute their lyrics to this site. Additionally, the site also serves as a community, in which both artists and listeners can select verses from lyrics and annotate and interpret rappers’ play on words, disses to other artists, and their overall, unique rhetoric.
I decided to use Voyant, as I wanted to document each album’s number count of unique words, a term used in the original study– in which multiple presentations of the same word is counted once. Admittedly, I had to blush once I had uploaded the lyrics to one song–to find expletive words profoundly appear on Voyant’s cirrus. To make the assignment appropriate, I have ranked the same five albums, based on its unique word forms, with its corresponding album covers.
10,095 total words
1,745 unique word forms
Invasion of Privacy, Cardi B
7,727 total words
1,418 unique word forms
ASTROWORLD, Travis Scott
5,710 total words
1,244 unique word forms
beerbongs & bentleys, Post Malone
8,707 total words
1,178 unique word forms
Stoney, Post Malone
6,814 total words
983 unique word forms
Initially, I was excited to drop Genius URLs directly into Voyant’s field box and quickly collect data. However, I soon realized Voyant collected all text from the site– including the site’s header, breaking text such as [Chorus], [Verse 1], [Bridge], usernames on the side, annotations and supplemental articles listed to the side of the lyrics. To create a more accurate word count, I created a Google document for each album, and copied and pasted the lyrics directly there. Doing so also helped with slimming down the text, as there were many featured artists and sampled artists that I did not want to inflate the primary artists’ unique word count. I then selected the entire text of each document into Voyant, and I immediately collected the unique word count of each album. Overall, Voyant was a straightforward guide for this assignment, but my learning lesson here was the importance of organizing the data first, as it was tempting to drop the links to each lyric on the field box, but only to collect inaccurate word counts.
Looking back now, I would have liked to explore this same project in my own form of a data visualization. Granted, many of the lyrics are inappropriate to discuss in an academic setting, and I am a bit embarrassed to share the Voyant links of my album documents, but there are other ways to present data.
I strongly appreciate Daniels’ visualizations for his essay, in which he presents an image of the rapper, and from the motion of the cursor, viewers can discover each of their metrics. I plan to render a a data set similar to this, one day.