Semantic Computing of Moods Based on Tags in Social Media of Music

TL;DR

This paper introduces the Affective Circumplex Transformation (ACT), a new semantic computing technique that effectively models music moods using social tags, outperforming existing methods and enabling large-scale mood analysis in music recommendation systems.

Contribution

The paper presents ACT, a novel and robust method for representing music moods based on social tags, validated through superior prediction accuracy over baseline models.

Findings

ACT outperforms baseline models in mood prediction.

Performance remains robust with fewer mood tags.

Enables large-scale mood analysis in social media music data.

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.