Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. 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. We take advantage of state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. In this work, we study the association between song lyrics and mood through a data-driven analysis. The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood Abstract
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