Mood Analysis during the COVID-19 Pandemic in Brazil through Music
Resumo
In this paper, we investigate the oscillation in the general feelings of the Brazilian population during the Pandemic through the songs consumed. We analyze Brazilian streaming musical consumption between 2019 and 2021. In special, we focus on special dates that have changed history, such as the beginning of the pandemic in the country, the dates of increase in cases, milestone dates in deaths, the beginning of vaccination, among others. Data was collected through Spotify API and made publicly available. Our results show people have preferred more danceable and positive songs during the period analyzed.
Palavras-chave:
mood analysis, COVID-19, Brazil, music
Referências
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Pedro Rego Ballona et al. 2015. Analyzing The Influence Of Pope’s Tweets On His Followers’ Mood. In WebMedia. ACM, 93–100. https://doi.org/10.1145/2820426.2820441
L. A. Clark and D. Watson. 1988. Mood and the mundane: relations between daily life events and self-reported mood. Journal of personality and social psychology 54 (1988), 296–308.
Arthur Emanuel de Oliveira Carosia et al. 2019. The influence of tweets and news on the brazilian stock market through sentiment analysis. In WebMedia. ACM, 385–392. https://doi.org/10.1145/3323503.3349564
J. S. Downie and Sally Jo Cunningham. 2002. Toward a Theory of Music Information Retrieval Queries: System Design Implications. In ISMIR. Paris, France.
K. M. Fritz and P. J O’Connor. 2016. Acute Exercise Improves Mood and Motivation in Young Men with ADHD Symptoms. Medicine and science in sports and exercise, (2016).
Clayton J. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In ICWSM.
Meijun Liu et al. 2020. Pandemics, Music, and Collective Sentiment: Evidence from the Outbreak of COVID-19. In ISMIR. Montréal, Canada.
Igor Maffei Libonati Maia et al. 2021. A Sentiment-Based Multimodal Method to Detect Fake News. In WebMedia. ACM, 213–216. https://doi.org/10.1145/3470482.3479467
Markus Schedl. 2016. The LFM-1b Dataset for Music Retrieval and Recommendation. In ICMR. ACM, 103–110. https://doi.org/10.1145/2911996.2912004
Markus Schedl et al. 2018. The Effects of Real-world Events on Music Listening Behavior: An Intervention Time Series Analysis. In Companion Procs of the WWW. 75–76. https://doi.org/10.1145/3184558.3186936
A. A. Stone and J. M. Neale. 1984. Effects of severe daily events on mood. Journal of personality and social psychology (1984).
Pedro Rego Ballona et al. 2015. Analyzing The Influence Of Pope’s Tweets On His Followers’ Mood. In WebMedia. ACM, 93–100. https://doi.org/10.1145/2820426.2820441
L. A. Clark and D. Watson. 1988. Mood and the mundane: relations between daily life events and self-reported mood. Journal of personality and social psychology 54 (1988), 296–308.
Arthur Emanuel de Oliveira Carosia et al. 2019. The influence of tweets and news on the brazilian stock market through sentiment analysis. In WebMedia. ACM, 385–392. https://doi.org/10.1145/3323503.3349564
J. S. Downie and Sally Jo Cunningham. 2002. Toward a Theory of Music Information Retrieval Queries: System Design Implications. In ISMIR. Paris, France.
K. M. Fritz and P. J O’Connor. 2016. Acute Exercise Improves Mood and Motivation in Young Men with ADHD Symptoms. Medicine and science in sports and exercise, (2016).
Clayton J. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In ICWSM.
Meijun Liu et al. 2020. Pandemics, Music, and Collective Sentiment: Evidence from the Outbreak of COVID-19. In ISMIR. Montréal, Canada.
Igor Maffei Libonati Maia et al. 2021. A Sentiment-Based Multimodal Method to Detect Fake News. In WebMedia. ACM, 213–216. https://doi.org/10.1145/3470482.3479467
Markus Schedl. 2016. The LFM-1b Dataset for Music Retrieval and Recommendation. In ICMR. ACM, 103–110. https://doi.org/10.1145/2911996.2912004
Markus Schedl et al. 2018. The Effects of Real-world Events on Music Listening Behavior: An Intervention Time Series Analysis. In Companion Procs of the WWW. 75–76. https://doi.org/10.1145/3184558.3186936
A. A. Stone and J. M. Neale. 1984. Effects of severe daily events on mood. Journal of personality and social psychology (1984).
Publicado
07/11/2022
Como Citar
PAULA, Bruna C. M.; OLIVEIRA, Gabriel P.; MORO, Mirella M..
Mood Analysis during the COVID-19 Pandemic in Brazil through Music. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 53-56.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2022.227063.
