  As a modern musician and cultural icon, Taylor Swift has earned worldwide
acclaim via pieces which predominantly draw upon the complex dynamics of
personal and interpersonal experiences. Here we show, for the first time, how
Swift's lyrical and melodic structure have evolved in their representation of
emotions over a timescale of $\tau\sim14$ yr. Previous progress on this topic
has been challenging based on the sheer volume of the relevant discography, and
that uniquely identifying a song that optimally describes a hypothetical
emotional state represents a multi-dimensional and complex task. To quantify
the emotional state of a song, we separate the criteria into the level of
optimism ($H$) and the strength of commitment to a relationship ($R$), based on
lyrics and chordal tones. We apply these criteria to a set of 149 pieces
spanning almost the entire repertoire. We find an overall trend toward positive
emotions in stronger relationships, with a best-fit linear relationship of
$R=0.642^{+0.086}_{-0.053}H-1.74^{+0.39}_{-0.29}$. We find no significant
trends in mean happiness ($H$) within individual albums over time. The mean
relationship score ($R$) shows trends which we speculate may be due to age and
the global pandemic. We provide tentative indications that partners with blue
eyes and/or bad reputations may lead to overall less positive emotions, while
those with green or indigo-colored eyes may produce more positive emotions and
stronger relationships. However, we stress that these trends are based on small
sample sizes, and more data are necessary to validate them. Finally, we present
the taylorswift python package which can be used to optimize song selection
according to a specific mood.
