  In this work, we study the association between song lyrics and mood through a
data-driven analysis. Our data set consists of nearly one million songs, with
song-mood associations derived from user playlists on the Spotify streaming
platform. We take advantage of state-of-the-art natural language processing
models based on transformers to learn the association between the lyrics and
moods. We find that a pretrained transformer-based language model in a
zero-shot setting -- i.e., out of the box with no further training on our data
-- is powerful for capturing song-mood associations. Moreover, we illustrate
that training on song-mood associations results in a highly accurate model that
predicts these associations for unseen songs. Furthermore, by comparing the
prediction of a model using lyrics with one using acoustic features, we observe
that the relative importance of lyrics for mood prediction in comparison with
acoustics depends on the specific mood. Finally, we verify if the models are
capturing the same information about lyrics and acoustics as humans through an
annotation task where we obtain human judgments of mood-song relevance based on
lyrics and acoustics.
