MuSe: The Musical Sentiment Dataset

The MuSe (Music Sentiment) dataset contains sentiment information for 90,408 songs. We computed scores for the affective dimensions of <em>valence</em>, <em>dominance</em> and <em>arousal</em>, based on the user-generated tags that are available for each song via Last.fm. In addition, we provide artist and title metadata as well as a Spotify ID and a MusicBrainz ID, which allow researchers to extend the dataset with further metadata, such as genre or year. Though the tags themselves cannot be included in the dataset, we include a jupyter notebook in our accompanying Github repository that demonstrates how to fetch the tags of a given song from the Last.fm API (Last.fm_API.ipynb) We further include a jupyter notebook in the same repository that demonstrates how one might enrich the dataset with audio features using different endpoints of the Spotify API using the included Spotify IDs (spotify_API.ipynb). Please note that in its current form, the dataset only contains tentative spotify IDs for a subset (around 68%) of the songs.