Are Expressions for Music Emotions the Same Across Cultures?

2025-02-12

AI Summary

This study investigates the cross-cultural universality of musical emotion expression. To address this, we conducted nine online experiments across Brazil, the United States, and South Korea (N = 672), employing culturally balanced sampling of local popular music and open-ended labeling to construct a trilingual emotion lexicon, followed by cross-cultural annotation and semantic similarity modeling. Methodologically, we introduce a novel “domain-sensitive, bottom-up” paradigm for musical emotion elicitation. Our analyses reveal, for the first time, systematic failures of machine translation in preserving musical emotion semantics. Results demonstrate cross-cultural consistency for high-arousal, high-valence emotions (e.g., excitement, joy), whereas medium- to low-arousal emotions (e.g., nostalgia, melancholy) exhibit significant cultural divergence. These findings provide both methodological foundations and empirical evidence for cross-cultural music cognition research and AI-driven affective modeling.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Music evokes profound emotions, yet the universality of emotional descriptors across languages remains debated. A key challenge in cross-cultural research on music emotion is biased stimulus selection and manual curation of taxonomies, predominantly relying on Western music and languages. To address this, we propose a balanced experimental design with nine online experiments in Brazil, the US, and South Korea, involving N=672 participants. First, we sample a balanced set of popular music from these countries. Using an open-ended tagging pipeline, we then gather emotion terms to create culture-specific taxonomies. Finally, using these bottom-up taxonomies, participants rate emotions of each song. This allows us to map emotional similarities within and across cultures. Results show consistency in high arousal, high valence emotions but greater variability in others. Notably, machine translations were often inadequate to capture music-specific meanings. These findings together highlight the need for a domain-sensitive, open-ended, bottom-up emotion elicitation approach to reduce cultural biases in emotion research.

Problem

• Cross-cultural music emotion universality
• Biased stimulus selection in research
• Inadequacy of machine translations

Innovation

• Balanced cross-cultural music selection
• Open-ended emotion tagging pipeline
• Culture-specific bottom-up taxonomies