Predicting the Preference for Sad Music: The Role of Gender, Personality, and Audio Features

• Published in: IEEE access Vol. 9; pp. 92952 - 92963
• Main Authors: Xu, Liuchang, Zheng, Ye, Xu, Dayu, Xu, Liang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: audio signal processing; Emotion recognition; Gender; individual difference; Machine learning; Multiple signal classification; Music; music emotion perception; music preference; music recommendation; Personality; Prediction models; Predictive models; Preferences; Psychology; Recommender systems; Rocks; Sad music; Statistical analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3090940

The "tragedy paradox" of music, avoiding experiencing negative emotions but enjoying the sadness portrayed in music, has attracted a great deal of academic attention in recent decades. Combining experimental psychology research methods and machine learning techniques, this study (a) investigated the effects of gender and Big Five personality factors on the preference for sad music in the Chinese social environment and (b) constructed sad music preference prediction models using audio features and individual features as inputs. Statistical analysis found that males have a greater preference for sad music than females do, and that gender and the extraversion factor are involved in significant two-way interactions. The best-performing random forest regression shows a low predictive effect on the preference for sad music (<inline-formula> <tex-math notation="LaTeX">R^{2} =0.138 </tex-math></inline-formula>), providing references for music recommendation systems. Finally, the importance-based model interpretation feature reveals that, in addition to the same music inputs (audio features), the perceived relaxation and happiness of music play an important role in the prediction of sad music preferences.