  This study explores the association between music preferences and moral
values by applying text analysis techniques to lyrics. Harvesting data from a
Facebook-hosted application, we align psychometric scores of 1,386 users to
lyrics from the top 5 songs of their preferred music artists as emerged from
Facebook Page Likes. We extract a set of lyrical features related to each
song's overarching narrative, moral valence, sentiment, and emotion. A machine
learning framework was designed to exploit regression approaches and evaluate
the predictive power of lyrical features for inferring moral values. Results
suggest that lyrics from top songs of artists people like inform their
morality. Virtues of hierarchy and tradition achieve higher prediction scores
($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq
.11$), while basic demographic variables only account for a small part in the
models' explainability. This shows the importance of music listening
behaviours, as assessed via lyrical preferences, alone in capturing moral
values. We discuss the technological and musicological implications and
possible future improvements.
