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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7795573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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