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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Bibliographic Details
Published in:International journal of environmental research and public health 2020-12, Vol.18 (1), p.218
Main Authors: Aljameel, Sumayh S, Alabbad, Dina A, Alzahrani, Norah A, Alqarni, Shouq M, Alamoudi, Fatimah A, Babili, Lana M, Aljaafary, Somiah K, Alshamrani, Fatima M
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Containment
Coronaviruses
COVID-19
COVID-19 - prevention & control
Datasets
Disease Outbreaks - prevention & control
Health Knowledge, Attitudes, Practice
Humans
Learning algorithms
Model accuracy
Outbreaks
Prediction models
Public Health
Saudi Arabia - epidemiology
Sentiment analysis
Social networks
Support Vector Machine
Support vector machines
Viral diseases
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph18010218