A Research on the Perception of Emergency Remote Teaching at Federal Institutes based on Opinion Mining

• Mateus F. L. Souza IFMG
• Cristiane N. Targa IFMG
• Carlos A. Silva IFMG

DOI: https://doi.org/10.5753/encompif.2022.223080

Abstract

Emergency Remote Teaching, the ERE, is a pedagogical strategy adopted by educational institutions around the world to reduce the impacts caused by Covid-19. In this period the use of social networks intensified, becoming one of the main sources of information, entertainment and outbursts. This research presents a study on the perception and feeling of the academic community of federal institutes in the period comprising the ERE from 2020 to 2021, using opinion mining techniques. We built a database collected from Twitter and applied the Multinomial Naive Bayes method to categorize feelings. It can be seen that in the period from June to August 2020 the community had a positive feeling.

Keywords: Sentiment Analysis, Federal Institute, Pandemic, Remote Learning

References

Alam, S. and Yao, N. (2019). The impact of preprocessing steps on the accuracy of machine learning algorithms in sentiment analysis. Computational and Mathematical Organization Theory, 25(3):319–335.

Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M., and Varma, V. (2012). Mining sentiments from tweets. In WASSA@ ACL, pages 11–18.

Benevenuto, F., Ribeiro, F., and Araújo, M. (2015). Métodos para análise de sentimentos em mídias sociais. Sociedade Brasileira de Computação.

Castilho, M. L. and da Silva, C. N. N. (2020). A covid-19 e a educação profissional e tecnológica: um panorama das ações de acompanhamento e enfrentamento da pandemia nos institutos federais. Revista Nova Paideia-Revista Interdisciplinar em Educação e Pesquisa, 2(3):18–34.

Gholamy, A., Kreinovich, V., and Kosheleva, O. (2018). Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. Technical report, University of Texas at El Paso.

Haddi, E., Liu, X., and Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia computer science, 17:26–32.

Moraes, S. M., Manssour, I. H., and Silveira, M. S. (2015). 7x1pt: um corpus extraído do twitter para análise de sentimentos em língua portuguesa. In Anais do X Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 21–25. SBC.

Oliveira, E. A. M. and Silva, C. A. (2022). Ensino Remoto Emergencial (ERE): múltiplas visões e vivências no ensino técnico e tecnológico em tempos de pandemia, volume 1, chapter Os desafios da gestão pedagógica na vigência do Ensino Remoto Emergencial em 2020, pages 37–51. Editora Fi, Porto Alegre, 1 edition.

Rodrigues, A. O., de Melo-Minardi, R. C., and Junior, W. M. (2013). Como nos sentimos: Uma ferramenta de mineração visual de sentimentos no twitter. In Anais do II Brazilian Workshop on Social Network Analysis and Mining, pages 91–102. SBC.