Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization

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dc.contributor.authorJojoa Acosta, Mario Fernando
dc.contributor.authorEftekhar, Parvin
dc.contributor.authorNowrouzi-Kia, Behdin
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2025-05-09T12:19:31Z
dc.date.available2025-05-09T12:19:31Z
dc.date.issued2024-06
dc.date.updated2025-05-09T12:19:31Z
dc.description.abstractCOVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803en
dc.identifier.citationJojoa, M., Eftekhar, P., Nowrouzi-Kia, B., & Garcia-Zapirain, B. (2024). Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization. AI and Society, 39(3), 883-890. https://doi.org/10.1007/S00146-022-01594-W
dc.identifier.doi10.1007/S00146-022-01594-W
dc.identifier.eissn1435-5655
dc.identifier.issn0951-5666
dc.identifier.urihttps://hdl.handle.net/20.500.14454/2708
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2022
dc.subject.otherDeep learning
dc.subject.otherDistilBERT
dc.subject.otherNatural language processing
dc.subject.otherSentiment analysis
dc.subject.otherTransformer
dc.titleNatural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorizationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage890
oaire.citation.issue3
oaire.citation.startPage883
oaire.citation.titleAI and Society
oaire.citation.volume39
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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