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A New Hybridization of Evolutionary Algorithms, GRASP and Set-Partitioning Formulation for the Capacitated Vehicle Routing Problem Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Identifying Fine-Grained Opinion and Classifying Polarity on Coronavirus Pandemic

Authors : Francielle Alves Vargas, Rodolfo Sanches Saraiva Dos Santos, Pedro Regattieri Rocha

Published in: Intelligent Systems

Publisher: Springer International Publishing

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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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Footnotes
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Metadata
Title
Identifying Fine-Grained Opinion and Classifying Polarity on Coronavirus Pandemic
Authors
Francielle Alves Vargas
Rodolfo Sanches Saraiva Dos Santos
Pedro Regattieri Rocha
Copyright Year
2020
Book
Intelligent Systems

Print ISBN: 978-3-030-61376-1

Electronic ISBN: 978-3-030-61377-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-61377-8_35

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