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

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Publication Details
Author list: Sumayh S. Aljameel, Dina A. Alabbad, Norah A. Alzahrani, Shouq M. Alqarni, Fatimah A. Alamoudi, Lana M. Babili, Somiah K. Aljaafary, and Fatima M. Alshamrani
Publisher: MDPI
Publication year: 2021
Journal: International Journal of Environmental Research and Public Health
Journal acronym: IJERPH
Volume number: 18
Issue number: 1
Start page: 1
End page: 12
Number of pages: 12
ISSN: 1661-7827
Web of Science ID: 000606219600001
PubMed ID: 33396713
Scopus ID: 85098860634
eISSN: 1660-4601

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), Knearest 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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Last updated on 2021-22-03 at 14:01