# Are Expressions for Music Emotions the Same Across Cultures?

arXiv: https://arxiv.org/abs/2502.08744 · HTML: https://mlsanity.com/paper/2502.08744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 45 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Gibbs Sampling with People](https://mlsanity.com/paper/2008.02595/) (2020)
- [Words are all you need? Language as an approximation for human similarity judgments](https://mlsanity.com/paper/2206.04105/) (2022)
- [Cross-cultural Mood Perception in Pop Songs and its Alignment with Mood Detection Algorithms](https://mlsanity.com/paper/2108.00768/) (2021)

## Built on by (most cited first, 1 of 1)

- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)

## Abstract (arXiv)

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.