Abstract
In this paper, we explore the fine-grained opinion identification and polarity classification tasks using twitter data on the COVID-19 pandemic in Brazilian Portuguese. We trained machine learning-based classifiers using a few different methods and tested how well they performed different tasks. For polarity classification, we tested a cross-domain strategy in order to measure the performance of the classifiers among different domains. For fine-grained opinion identification, we provide a taxonomy of opinion aspects and employed them in conjunction with machine learning methods. Based on the obtained results, we found that the cross-domain data improved the results of the polarity classification. For fine-grained opinion identification, the use of a domain taxonomy presented competitive results for the Portuguese language.
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The authors are grateful to CAPES and CNPq for supporting this work.
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Vargas, F.A., Dos Santos, R.S.S., Rocha, P.R. (2020). Identifying Fine-Grained Opinion and Classifying Polarity on Coronavirus Pandemic. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_35
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