TCC - Bacharelado em Sistemas de Informação (UAST)

URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2936

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Analisando a opinião de usuários do Twitter em relação ao lockdown no Brasil: um estudo frente à mídia convencional
(2022-05-23) Silva, Paulo Alves da; Belo, Ítalo César de Souza; Oliveira, Marcelo Iury de Sousa; http://lattes.cnpq.br/2328386382232459; http://lattes.cnpq.br/3240928244006916; Não localizado
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
Explorando o sentimento no twitter sobre a implementação do ensino virtual no Brasil em face da covid-19 .
(2022-05-27) Leite, Felipe Antonio Alves; Belo, Ítalo César de Souza; Oliveira, Marcelo Iury de Sousa; http://lattes.cnpq.br/2328386382232459; http://lattes.cnpq.br/3240928244006916; http://lattes.cnpq.br/2530370455196055
The high risk of COVID-19 contamination made several countries take social distancing and isolation measures. As a result of these policies, virtual education was the alternative chosen by many schools and higher education institutions to continue their classes. However, that situation, COVID-19-induced, raised some questions about the adequacy of virtual learning concerning students' and teachers' expectations. This work conducted a study of sentiment analysis in Twitter messages to understand the feeling of Brazilian users (teachers and students) about the first months of virtual education in Brazil during a COVID-19 pandemic. Also, it was sought to identify the factors responsible for certain feelings and the consequences of virtual teaching at the beginning of the pandemic. In order to achieve these objectives, sentiment analysis was performed using an ensemble and the Active Learning technique to ensure better accuracy in the classification of the data and, consequently, have an understanding of the current scenario. When analyzing the data, the rejection of teachers and students concerning virtual education during the pandemic was noticeable. It is essential to highlight that factors responsible for this rejection were identified, such as excessive activities, mental exhaustion, and procrastination. In addition, it was found that the adoption of emergency remote teaching had consequences such as anxiety crises, emotional stress, low self-esteem, sleep deprivation, and depression.