Analisando as emoções dos tweets relacionadas à Covid-19 no Rio de Janeiro

  • Gustavo F. L. Gonçalves UFF
  • Antonio A. de A. Rocha UFF
  • Aline Paes UFF


Este trabalho tem como objetivo analisar postagens de tweets relacionados à Covid-19 para mostrar quais foram as emoções predominantes dos usuários. Para isso coletamos tweets relacionados ao Rio de Janeiro e produzimos análises estatísticas e classificadores de emoções. Além disso, o artigo também traz insights sobre os assuntos discutidos pelos usuários e como as emoções mudaram de acordo com eventos específicos. As ferramentas aqui desenvolvidas ajudam a compreender os comportamentos e emoções dos usuários do Twitter, resultando em evidências que podem ser úteis em situações catastróficas semelhantes.


Baziotis, C., Pelekis, N., and Doulkeridis, C. (2017). Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. In Proc. of the 11th Int. Workshop on Semantic Evaluation (SemEval-2017), pages 747-754, Vancouver, Canada. Association for Computational Linguistics.

Brum, P. V., Teixeira, M. C., Miranda, R., Vimieiro, R., Jr, W. M., and Pappa, G. L. (2020). A characterization of portuguese tweets regarding the covid-19 pandemic. In Anais do VIII Symposium on Knowledge Discovery, Mining and Learning, pages 177-184, Porto Alegre, RS, Brasil. SBC.

Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., and Solorio, T., editors, Proc. of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACLHLT 2019, Volume 1 (Long and Short Papers), pages 4171-4186. ACL.

Harris, Z. S. (1954). Distributional structure. Word, 10(2-3):146-162.

Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrialstrength Natural Language Processing in Python.

Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2017). Bag of tricks for efficient text classification. In Lapata, M., Blunsom, P., and Koller, A., editors, Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics, EACL 2017, Volume 2: Short Papers, pages 427-431. Association for Computational Linguistics.

Ko, Y. (2012). A study of term weighting schemes using class information for text classification. In Proc. of the 35th Int. ACM SIGIR Conf. on Research and development in information retrieval, pages 1029-1030.

Li, I., Li, Y., Li, T., Alvarez-Napagao, S., Garcia-Gasulla, D., and Suzumura, T. (2020). What are we depressed about when we talk about covid-19: Mental health analysis on tweets using natural language processing. In Int. Conf. on Innovative Techniques and Applications of Artificial Intelligence, pages 358-370. Springer.

Loria, S. (2018). textblob documentation. Release 0.15, 2.

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. In Bengio, Y. and LeCun, Y., editors, 1st Int. Conf. on Learning Representations, ICLR 2013, Workshop Track Proceedings.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Burges, C. J. C., Bottou, L., Ghahramani, Z., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 26, pages 3111-3119.

Müller, M., Salathé, M., and Kummervold, P. E. (2020). Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter.

Nemes, L. and Kiss, A. (2021). Social media sentiment analysis based on covid-19. Journal of Information and Telecommunication, 5(1):1-15.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.

Plutchik, R. (1984). Emotions: A general psychoevolutionary theory. Approaches to emotion, 1984:197-219.

Roelleke, T. and Wang, J. (2008). Tf-idf uncovered: a study of theories and probabilities. In Proc. of the 31st annual Int. ACM SIGIR Conf. on Research and development in information retrieval, pages 435-442.

Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., and Choi, G. S. (2021). A performance comparison of supervised machine learning models for covid-19 tweets sentiment analysis. PloS one, 16(2):e0245909.

Scott, S. and Matwin, S. (1999). Feature engineering for text classification. In ICML, volume 99, pages 379-388. Citeseer.

Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conf. on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., von Luxburg, U., Bengio, S., Wallach, H. M., Fergus, R., Vishwanathan, S. V. N., and Garnett, R., editors, Advances in Neural Information Processing Systems 30: Annual Conf. on Neural Information Processing Systems 2017, pages 5998-6008.
Como Citar

Selecione um Formato
GONÇALVES, Gustavo F. L.; ROCHA, Antonio A. de A.; PAES, Aline. Analisando as emoções dos tweets relacionadas à Covid-19 no Rio de Janeiro. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 6. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 210-223. ISSN 2595-2706. DOI: