  The mood of a song is a highly relevant feature for exploration and
recommendation in large collections of music. These collections tend to require
automatic methods for predicting such moods. In this work, we show that
listening-based features outperform content-based ones when classifying moods:
embeddings obtained through matrix factorization of listening data appear to be
more informative of a track mood than embeddings based on its audio content. To
demonstrate this, we compile a subset of the Million Song Dataset, totalling
67k tracks, with expert annotations of 188 different moods collected from
AllMusic. Our results on this novel dataset not only expose the limitations of
current audio-based models, but also aim to foster further reproducible
research on this timely topic.
