Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Semantic computing of moods based on tags in social media of music.

Saari, P. and Eerola, T. (2014) 'Semantic computing of moods based on tags in social media of music.', IEEE transactions on knowledge and data engineering., 26 (10). pp. 2548-2560.

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.

Item Type:Article
Keywords:Semantic analysis, Social tags, Music, Music information retrieval, Moods, Genres, Prediction.
Full text:(AM) Accepted Manuscript
Download PDF
(874Kb)
Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1109/TKDE.2013.128
Publisher statement:© 2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Date accepted:No date available
Date deposited:22 October 2014
Date of first online publication:October 2014
Date first made open access:No date available

Save or Share this output

Export:
Export
Look up in GoogleScholar