  Social tags inherent in online music services such as Last.fm provide a rich
source of information on musical moods. The abundance of social tags makes this
data highly beneficial for developing techniques to manage and retrieve mood
information, and enables study of the relationships between music content and
mood representations with data substantially larger than that available for
conventional emotion research. However, no systematic assessment has been done
on the accuracy of social tags and derived semantic models at capturing mood
information in music. We propose a novel technique called Affective Circumplex
Transformation (ACT) for representing the moods of music tracks in an
interpretable and robust fashion based on semantic computing of social tags and
research in emotion modeling. We validate the technique by predicting listener
ratings of moods in music tracks, and compare the results to prediction with
the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative
Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA).
The results show that ACT consistently outperforms the baseline techniques, and
its performance is robust against a low number of track-level mood tags. The
results give validity and analytical insights for harnessing millions of music
tracks and associated mood data available through social tags in application
development.
