Description:
In this short paper we present work in progress that tries to leverage crowdsourced music metadataand crowdsourced affective word norms to create a comprehensive dataset of music emotions, whichcan be used for sentiment analyses in the music domain. We combine a mixture of different datasources to create a new dataset of 90,408 songs with their associated embeddings in Russell’s modelof affect, with the dimensions valence, dominance and arousal. In addition, we provide a Spotify IDfor the songs, which can be used to add more metadata to the dataset via the Spotify API.