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

COVID-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 lockd...

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Autores principales: Jojoa, Mario, Eftekhar, Parvin, Nowrouzi-Kia, Behdin, Garcia-Zapirain, Begonya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676868/
https://www.ncbi.nlm.nih.gov/pubmed/36439363
http://dx.doi.org/10.1007/s00146-022-01594-w
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author Jojoa, Mario
Eftekhar, Parvin
Nowrouzi-Kia, Behdin
Garcia-Zapirain, Begonya
author_facet Jojoa, Mario
Eftekhar, Parvin
Nowrouzi-Kia, Behdin
Garcia-Zapirain, Begonya
author_sort Jojoa, Mario
collection PubMed
description COVID-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: 0.803. 
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spelling pubmed-96768682022-11-21 Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization Jojoa, Mario Eftekhar, Parvin Nowrouzi-Kia, Behdin Garcia-Zapirain, Begonya AI Soc Original Paper COVID-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: 0.803.  Springer London 2022-11-21 /pmc/articles/PMC9676868/ /pubmed/36439363 http://dx.doi.org/10.1007/s00146-022-01594-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Jojoa, Mario
Eftekhar, Parvin
Nowrouzi-Kia, Behdin
Garcia-Zapirain, Begonya
Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title_full Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title_fullStr Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title_full_unstemmed Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title_short Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
title_sort natural language processing analysis applied to covid-19 open-text opinions using a distilbert model for sentiment categorization
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676868/
https://www.ncbi.nlm.nih.gov/pubmed/36439363
http://dx.doi.org/10.1007/s00146-022-01594-w
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