The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood
This work addresses the challenge of mood prediction in music for applications like streaming platforms, but it is incremental as it builds on existing NLP and acoustic methods without introducing new paradigms.
The study tackled the problem of understanding how song lyrics and acoustics associate with mood by analyzing nearly one million songs from Spotify playlists, finding that a pretrained transformer model in a zero-shot setting effectively captures these associations and that training on song-mood data yields a highly accurate predictive model.
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.