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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
| Content Provider | MDPI |
|---|---|
| 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. |
| Copyright Year | 2020 |
| 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. |
| Starting Page | 218 |
| e-ISSN | 16604601 |
| DOI | 10.3390/ijerph18010218 |
| Journal | International Journal of Environmental Research and Public Health |
| Issue Number | 1 |
| Volume Number | 18 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-30 |
| Access Restriction | Open |
| Subject Keyword | International Journal of Environmental Research and Public Health Medical Informatics Arabic Sentiment Analysis Machine Learning Support Vector Machine K-nearest Neighbor Naïve Bayes N-gram Natural Language Processing Twitter |
| Content Type | Text |
| Resource Type | Article |
