Abstract
Air pollution has imposed significant negative effects on individuals’ well-being, including citizens’ sentiment levels. To test this claim, we investigate the impact of air pollution on Chinese urbanites’ music sentiments. The analysis is based on a unique dataset of high-frequency music consumption records from a music platform in China from October 13th, 2019 to January 7th, 2020. Using machine learning algorithms, songs on this platform are divided into cheerful songs, melancholy songs and other categories, by which a music sentiment index (MSI) is generated at city-daily level. By matching MSI and daily air quality, this study finds that the MSI declines during highly polluted days, indicating that: on highly polluted days, citizens tend to enjoy melancholy songs over cheerful ones. In addition, this effect becomes more remarkable when the Air Quality Index (AQI) score is above 200, a critical point for “heavily polluted” and “severely polluted” days.
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