In this work we study the association between the song lyrics and \textit{mood} associations with the song, and conduct a data driven analysis to compare how lyrics and acoustics each contribute to the understanding of a song's mood. We conduct our study on a data set containing nearly one million songs, with song-mood associations derived from user playlists on a 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 model in 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 acoustics, we observe that the relative importance of lyrics 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 by conducting a manual annotation task on a hundred songs where we obtain human judgments of mood-song relevance based on lyrics and acoustics in isolation.

The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood

s3-2: Music

technical paper

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ICWSM 2022

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

Jussi Karlgren

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The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood

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