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9th Brazilian Conference on Intelligent Systems, BRACIS 2020 ; 12319 LNAI:511-520, 2020.
Article in English | Scopus | ID: covidwho-897935

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. © 2020, Springer Nature Switzerland AG.

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