This project is an idea that I don't intend to work on but would love to see someone tackle. I also want to preface this by stating that I enjoy Taylor Swift's music and this project is in no way intended to diminish her work.

Taylor Swift is currently on her Eras Tour, a tour comprised of 3.25 hour-long concerts that feature sets broken up by each of her first ten studio albums, which she refers to as "eras." However, her first ten albums were released between 2006 and 2022, meaning her "eras" lasted 1.6 years each on average -- hardly long enough to be called era! Moreover, some of her albums sound pretty darn similar to me, and I wonder how musically different these eras truly are.

This project might seem a bit silly in these, but analyzing Taylor Swift's music could use some pretty serious machine learning. My thought was to consider two aspects of each album: the music itself and her lyrics. Using a Python package like librosa (https://librosa.org/doc/latest/index.html), one could feasibly use clustering analysis or something similar to measure how different her albums are musically. Then one could use something like sentiment analysis on her lyrics to determine how much lyrical themes shift between albums. One could even consider these together by looking at a weighted average of the two measures.

Beyond simply analyzing Taylor Swift's music, similar techniques could be used on chart-topping pop music leading up to and immediately following the release of each of her albums to assess if her albums truly define eras or if they fit into more general pop music trends. This could potentially motivate economic questions about charting a pop star's path to enduring success.