Open AccessRelacionando Modelagem de Tópicos e Classificação de Sentimentos para Análise de Mensagens do Twitter Durante a Pandemia da COVID-19
Author(s) -
Matheus Adler Soares Pinto,
Antônio F. L. Jacob,
Antonio José G. Busson,
Sérgio Colcher
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/webmedia_estendido.2020.13064
Subject(s) - covid-19 , computer science , sentiment analysis , humanities , artificial intelligence , philosophy , medicine , disease , pathology , infectious disease (medical specialty)
In 2020, COVID-19 pandemic is one of the most talked-about subjects on social networks. This subject has generated discussions of great importance about politics, economics, medical advances, people’s awareness, preventive techniques, etc. Using sentiment analysis and topic modeling techniques, in this paper, we aim to present an analysis of the messages from the social network Twitter during the pandemic of COVID-19. For this, we use a tweets dataset to train a sentiment classifier and then use the NMF algorithm to perform the interest topic generation.