6533b85efe1ef96bd12c0879
RESEARCH PRODUCT
Semantic Computing of Moods Based on Tags in Social Media of Music
Pasi SaariTuomas Eerolasubject
FOS: Computer and information sciencesVocabularyComputer scienceMusic information retrievalmedia_common.quotation_subjectSemantic analysis (machine learning)Moodscomputer.software_genreAffect (psychology)SemanticsComputer Science - Information RetrievalSemantic computingMusic information retrievalAffective computingmedia_commonSocial and Information Networks (cs.SI)ta113Probabilistic latent semantic analysisSocial tagsbusiness.industryComputer Science - Social and Information NetworksMultimedia (cs.MM)Semantic analysisComputer Science ApplicationsMoodComputational Theory and MathematicsWeb miningta6131Vector space modelArtificial intelligenceGenresbusinesscomputerComputer Science - MultimediaInformation Retrieval (cs.IR)MusicNatural language processingPrediction.Information Systemsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-10-01 | IEEE Transactions on Knowledge and Data Engineering |