Explorando o sentimento no Twitter sobre a implementação do ensino remoto no Brasil em face à COVID-19
Resumo
O alto risco de contaminação do COVID-19 tem levado vários países a tomar medidas de distanciamento social e isolamento. Como resultado dessas políticas, o ensino remoto foi a alternativa escolhida por muitas escolas e instituições de ensino superior para dar continuidade às aulas e retomar os cursos. No entanto, essa digitalização induzida pelo COVID-19 levantou algumas questões sobre a aptidão do aprendizado remoto em relação às expectativas dos alunos. Este trabalho realizou um estudo de análise de sentimento em mensagens do Twitter com o objetivo de compreender o sentimento dos usuários brasileiros sobre os primeiros meses de introdução da educação remoto emergencial no Brasil durante uma pandemia de COVID-19.
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Yue, L., Chen, W., Li, X., Zuo, W., and Yin, M. (2018). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2):617–663.
Zimmermann, M., Ntoutsi, E., and Spiliopoulou, M. (2015). Incremental active opinion learning over a stream of opinionated documents. arXiv preprint arXiv:1509.01288.
Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of covid-19 pandemic. Higher Education Studies, 10(3):16–25.
Alswaidan, N. and Menai, M. E. B. (2020). A survey of state-of-the-art approaches for emotion recognition in text. Knowledge and Information Systems, 62(8):2937–2987.
Arruda, E. P. (2020). Educação remota emergencial: elementos para políticas públicas na educação brasileira em tempos de covid-19. EmRede-Revista de Educação a Distância, 7(1):257–275.
Camacho, A. C. L. F., Fuly, P., Santos, M., and Menezes, H. (2020). Alunos em vulnerabi-lidade social em disciplinas de educação a distância em tempos de covid-19. Research, Society and Development, 9:275973979.
Creswell, J. W. (2010). Projeto de pesquisa metodos qualitativo, quantitativo e misto. In Projeto de pesquisa metodos qualitativo, quantitativo e misto. Artmed.
Danka, T. and Horvath, P. (2018). modAL: A modular active learning framework for Python. available on arXiv at https://arxiv.org/abs/1805.00979.
Duong, V., Pham, P., Yang, T., Wang, Y., and Luo, J. (2020). The ivory tower lost: How college students respond differently than the general public to the covid-19 pandemic. arXiv preprint arXiv:2004.09968.
Galletta, D. F., Gaskin, J. E., Koch, H., Anderson, G., Jessup, L., King, J. L., Lowry, P. B., and Wetherbe, J. (2020). Educational disruption & rising faculty expectations. In Proceedings of AMCIS 2020.
Liu, B. and Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis, pages 415–463. Springer US, Boston, MA, EUA.
Moore, J. L., Dickson-Deane, C., and Galyen, K. (2011). e-learning, online learning, and distance learning environments: Are they the same? The Internet and Higher Education, 14(2):129–135.
Mostafa, L. (2021). Egyptian student sentiment analysis using word2vec during the coro-navirus (covid-19) pandemic. In Hassanien, A. E., Slowik, A., Snasel, V., El-Deeb, H., and Tolba, F. M., editors, Proceedings of the International Conference on Advanced In-telligent Systems and Informatics 2020, pages 195–203, Cham. Springer International Publishing.
Pastor, C. K. L. (2020). Sentiment analysis on synchronous online delivery of instruction due to extreme community quarantine in the philippines caused by covid-19 pandemic. Asian Journal of Multidisciplinary Studies, 3(1):1–6.
Souza, E., Castro, D., Vitorio, D., Teles, I., Oliveira, A. L. I., and Gusmão, C. (2016). Characterizing user-generated text content mining: A systematic mapping study of the portuguese language. In New Advances in Information Systems and Technologies, pages 1015–1024, Cham. Springer International Publishing.
Souza, E., Costa, D., Castro, D. W., Vitorio, D., Teles, I., Almeida, R., Alves, T., Oliveira, A. L. I., and Gusmao, C. (2018). Characterising text mining: a systematic mapping review of the portuguese language. IET Software, 12(2):49–75.
Vitorio, D., Souza, E., and Oliveira, A. L. I. (2019a). Evaluating active learning sampling strategies for opinion mining in brazilian politics corpora. In 19th EPIA Conference on Artificial Intelligence, pages 695–707, Cham. Springer.
Vitorio, D., Souza, E., and Oliveira, A. L. I. (2019b). Using active learning sampling strategies for ensemble generation on opinion mining. In 8th Brazilian Conference on Intelligent Systems (BRACIS), pages 114–119. IEEE.
Wang, D., Feng, S., Wang, D., and Yu, G. (2013). Detecting opinion drift from chi-nese web comments based on sentiment distribution computing. In Web Information Systems Engineering – WISE 2013, pages 72–81.
Wang, Z. and Cruz, I. F. (2020). Analysis of the impact of covid-19 on education based on geotagged twitter. COVID-19, page 15–23, New York, NY, USA. Association for Computing Machinery.
Watson, R., Corbett, J., Galletta, D. F., Ives, B., Mandviwalla, M., and Tremblay, M. (2020). Covid-19 and is: Challenges and opportunities for people, careers, and institu-tions. In Proceedings of AMCIS 2020.
Wozniak, M., Graña, M., and Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16:3 – 17.
Yin, R. K. (2013). Case study research: Design and methods. Sage publications.
Yu, H.-F., Huang, F.-L., and Lin, C.-J. (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning, 85(1-2):41–75.
Yue, L., Chen, W., Li, X., Zuo, W., and Yin, M. (2018). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2):617–663.
Zimmermann, M., Ntoutsi, E., and Spiliopoulou, M. (2015). Incremental active opinion learning over a stream of opinionated documents. arXiv preprint arXiv:1509.01288.
Publicado
25/10/2021
Como Citar
LEITE, Felipe A.; SILVA, Paulo Alves da; VITÓRIO, Douglas; OLIVEIRA, Marcelo Iury S..
Explorando o sentimento no Twitter sobre a implementação do ensino remoto no Brasil em face à COVID-19. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 9. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 132-143.
DOI: https://doi.org/10.5753/erigo.2021.18439.
