Measuring Emotions to Classify Songs: The Impact of the COVID-19 Pandemic on Music Streaming Data

Abstract

Data from music streaming has gained increasing attention since it allows studying music preferences across diverse cultures and different periods of time. Indeed, the study of “music and emotion” is crucial for understanding the psychological relationship between human sentiments and music. The temporal study of musical emotions provides beneficial insights into the analysis of the mood of listeners during periods of particular relevance and stress (e.g., the COVID-19 pandemic). This study performs music streaming data analysis to retrieve the musical emotions of the top Italian streamed songs during the pandemic. To this end, we propose two new indices for measuring anger and joy in songs. We suggest a procedure for clustering music streaming data: the DISTATIS procedure and Partitioning Around Medoids (PAM) clustering algorithm are sequentially applied to identify intervals of time sharing similar sentiments. Finally, we employ the proposed procedure to investigate the relationship between the evolution of the pandemic spread and sentiments extracted from songs. The results show that music streaming data analysis allow identifying five clusters of time intervals sharing similar sentiments, related to the evolution of the Italian restrictive quarantine measures.