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A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. Th...

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Autores principales: Aljameel, Sumayh S., Alabbad, Dina A., Alzahrani, Norah A., Alqarni, Shouq M., Alamoudi, Fatimah A., Babili, Lana M., Aljaafary, Somiah K., Alshamrani, Fatima M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795573/
https://www.ncbi.nlm.nih.gov/pubmed/33396713
http://dx.doi.org/10.3390/ijerph18010218
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author Aljameel, Sumayh S.
Alabbad, Dina A.
Alzahrani, Norah A.
Alqarni, Shouq M.
Alamoudi, Fatimah A.
Babili, Lana M.
Aljaafary, Somiah K.
Alshamrani, Fatima M.
author_facet Aljameel, Sumayh S.
Alabbad, Dina A.
Alzahrani, Norah A.
Alqarni, Shouq M.
Alamoudi, Fatimah A.
Babili, Lana M.
Aljaafary, Somiah K.
Alshamrani, Fatima M.
author_sort Aljameel, Sumayh S.
collection PubMed
description In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.
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spelling pubmed-77955732021-01-10 A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia Aljameel, Sumayh S. Alabbad, Dina A. Alzahrani, Norah A. Alqarni, Shouq M. Alamoudi, Fatimah A. Babili, Lana M. Aljaafary, Somiah K. Alshamrani, Fatima M. Int J Environ Res Public Health Article In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic. MDPI 2020-12-30 2021-01 /pmc/articles/PMC7795573/ /pubmed/33396713 http://dx.doi.org/10.3390/ijerph18010218 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aljameel, Sumayh S.
Alabbad, Dina A.
Alzahrani, Norah A.
Alqarni, Shouq M.
Alamoudi, Fatimah A.
Babili, Lana M.
Aljaafary, Somiah K.
Alshamrani, Fatima M.
A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title_full A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title_fullStr A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title_full_unstemmed A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title_short A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia
title_sort sentiment analysis approach to predict an individual’s awareness of the precautionary procedures to prevent covid-19 outbreaks in saudi arabia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795573/
https://www.ncbi.nlm.nih.gov/pubmed/33396713
http://dx.doi.org/10.3390/ijerph18010218
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