Where does the data come from?
The Spotify API holds descriptive audio characteristics* for songs. I gathered this data for every Taylor’s Version and its first version on the deluxe editions: 20 songs from Fearless (I’m considering “If This Was A Movie” to be a Fearless song), 20 from Red, and 16 from Speak Now.
The variables I chose are:
1) Acousticness: a measure between 0 and 1 of how “acoustic” a track is. The closer to 1, the more acoustic.
2) Danceability: a measure between 0 and 1 of how well the track can be used for dancing (closer to 1 means more danceable). It takes into account metrics like tempo and rhythm.
3) Energy: a measure between 0 and 1 of intensity within a track (“energetic tracks feel fast, loud, and noisy”). A higher score means a higher energy.
4) Instrumentalness: a measure between 0 and 1 of whether a track is instrumental. A measure closer to 1 is a higher likelihood of no vocals.
5) Speechness: a measure between 0 and 1 detecting spoken word within a track. The higher the measure, the higher the probability that the track is spoken word.
6) Valence: a measure between 0 and 1 describing the “musical positiveness” of a track (0: most sad vs. 1: most happy-sounding).
7) Loudness: The overall loudness of a track, in decibels.
Every variable in the data set ranges between 0 and 1, except for loudness. To make sure every variable was on the same scale, I normalized the loudness over each pair of albums to range between 0 and 1.
I chose to exclude metrics like liveness, which detects the presence of an audience within a track (none of the songs were recorded live), and things like tempo, key, and time signature which remain consistent between both versions.
(See link above for the full definitions of the variables).
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