Text mining the Billboard Country Top 10

My apologies to anyone who read this before the evening of October 8. I set this to post automatically, but for the wrong date and without all that I wanted to include.

I’m a big fan of music but as I’ve gotten further away from my undergrad years, I’ve become less familiar with what is currently playing on the radio. Thanks to my brother’s children, I have some semblance of a grasp on certain musical genres, but I have absolutely no idea what’s happening in the world of country music (I did at one point, as I went to undergrad in Virginia).

I decided to use Voyant Tools to do a text analysis of the first 10 songs on the Billboard Country chart from the week of September 8, 2018. The joke about country music is that it’s about dogs, trucks, and your wife leaving you. When I was more familiar with country music, I found it to be more complex than this, but a lot could have changed since I last paid attention. Will a look at the country songs with the most sales/airplay during this week support these assumptions? For the sake of uniformity, I accepted the lyrics on Genius.com as being correct and removed all extraneous words from the lyrics (chorus, bridge, etc.).

The songs in the top 10 are as follows:

  1. Meant to Be – Bebe Rexha & Florida Georgia Line
  2. Tequila – Dan + Shay
  3. Simple – Florida Georgia Line
  4. Drowns the Whiskey – Jason Aldean featuring Miranda Lambert
  5. Sunrise, Sunburn, Sunset – Luke Bryan
  6. Life Changes – Thomas Rhett
  7. Heaven – Kane Brown
  8. Mercy – Brett Young
  9. Get Along – Kenny Chesney
  10. Hotel Key – Old Dominion

If you would like to view these lyrics for yourself, I’ve left the files in a google folder.

As we can see, the words “truck,” “dog,” “wife,” and “left” were not among the most frequently used, although it may not be entirely surprising that “ain’t” was.

The most frequently used word in the corpus, “it’s” appeared only 19 times, showing that there is a quite a bit of diversity in these lyrics. I looked for other patterns, such as whether vocabulary density or average words per sentence had an effect on the song’s position on the chart, but there was no correlation.

2 thoughts on “Text mining the Billboard Country Top 10

  1. Sarah Garnett Kinniburgh


    I love this topic — lyrics are a fascinating way to explore text analysis methods! Your investigation got me thinking about something that has stuck out to me about country/pop music: the figures of the lyricist and the songwriter (who write, whether for a company or independently, specifically with the goal of getting others to perform their work), as well as the relatively high level of co-writing credits in contemporary country. I have a feeling that a few of the same people may have written multiple top 10 songs on the Billboard Country chart, so whose lyrics are we looking at? It would be really interesting if there was a way to create a more sophisticated, interactive version of I Write Like (https://iwl.me/) for lyrical motifs or audio signatures to analyze who wrote what, particularly in genres like contemporary country where sometimes — for better or for worse, and for so many reasons we can get into later — a lot of the songs tend to sound very similar.

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