Semantic Computing of Moods Based on Tags in Social Media of Music by Pasi Saari, Tuomas Eerola. (IEEE Transactions on Knowledge and Data Engineering, 2013; : 1 DOI: 10.1109/TKDE.2013.128)
Abstract:
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.
These results make me wonder if the results of tagging represents the average semantic resolution that users want?
Obviously a musician or musicologist would want far finer and sharper distinctions, at least for music of interest to them. Or substitute the domain of your choice. Domain experts want precision, while the average user muddles along with coarser divisions.
We already know from Karen Drabenstott’s work (Subject Headings and the Semantic Web) that library classification systems are too complex for the average user and even most librarians.
On the other hand, we all have some sense of the wasted time and effort caused by the uncharted semantic sea where Google and others practice catch and release with semantic data.
Some of the unanswered questions that remain:
How much semantic detail is enough?
For which domains?
Who will pay for gathering it?
What economic model is best?