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Data from music streaming has gained increasing attention since it allowsstudying music preferences across diverse cultures and different periods of time.Indeed, the study of “music and emotion” is crucial for understanding the psychologicalrelationship between human sentiments and music. The temporal studyof musical emotions provides beneficial insights into the analysis of the mood oflisteners during periods of particular relevance and stress (e.g., the COVID-19pandemic). This study performs music streaming data analysis to retrieve themusical emotions of the top Italian streamed songs during the pandemic. To thisend, we propose two new indices for measuring anger and joy in songs. We suggesta procedure for clustering music streaming data: the DISTATIS procedureand Partitioning Around Medoids (PAM) clustering algorithm are sequentiallyapplied to identify intervals of time sharing similar sentiments. Finally, we employthe proposed procedure to investigate the relationship between the evolutionof the pandemic spread and sentiments extracted from songs. The results show that music streaming data analysis allow identifying fiveclusters of time intervals sharing similar sentiments,
Albano et al. (Wed,) studied this question.