Analisando a opinião de usuários do Twitter em relação ao lockdown no Brasil: um estudo frente à mídia convencional

Abstract

In the pandemic scenario, several countries sought alternatives to reduce the high rates of contagion and mortality caused by COVID-19. Among the means used was lockdown, which is characterized as the most stringent measure of social isolation. Although lockdown is considered by many to be an excellent tool for health protection, the adoption of this measure divides opinions. In this sense, sentiment analysis emerges as a mechanism that can be used to understand the population's opinion about the lockdown. The social impact caused by this measure contributes to the emergence of articles from the most diverse media vehicles, which can strongly influence the critical thinking of the population. In this context, the present work performed a sentiment analysis to understand how Twitter users reacted to the lockdown implementation in Brazil during the COVID-19 pandemic. In addition, the results of the Twitter analysis were compared with the content coming from the gray literature, focusing on the articles authored by the conventional media, which addressed information about the theme. To carry out this research, data were extracted from the social network Twitter and processed through an ensemble composed of the following machine learning algorithms: Multinomial Naïve Bayes, SVM, Random Forest, MLP neural network, and the Logistic Regression classifier. In addition, Active Learning techniques, through the uncertainty approach, were combined with the ensemble to obtain higher accuracy of the classified data. According to the results, an interesting predominance of sentiments in favor of lockdown was noticed, as well as conflicts between the data obtained by the Twitter sentiment analysis process and information published in the gray literature