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RESEARCH PRODUCT

Impact of the COVID-19 pandemic on music: a method for clustering sentiments

Alessandro AlbanoMariangela SciandraAntonella PlaiaIrene Carola Spera

subject

Anger indexBeta regressionJoy indexCovid-19Settore SECS-S/01 - StatisticaHierarchical clustering

description

The outbreak of coronavirus disease 2019 (COVID-19) was highly stressful for people. In general, fear and anxiety about a disease can be overwhelming and cause strong emotions in adults and children. One way to cope with this stress consists in listening to music. Aim of this work is to understand if the music heard during the lock-down reflects the emotions generated by the pandemic on each of us. So, the primary goal of this work is to build two indices for measuring the anger and joy levels of the top streamed songs by Italian Spotify users (during the SARS-CoV-2 pandemic), and study their evolution over time. A Hierarchical Cluster Analysis has been applied in order to identify groups of weeks reflecting common musical sentiments, and a Beta regression model is used to validate the results of cluster analysis.

yearjournalcountryeditionlanguage
2021-10-01
http://hdl.handle.net/10447/524502