PMID- 33396713
OWN - NLM
STAT- MEDLINE
DCOM- 20210111
LR  - 20210112
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 18
IP  - 1
DP  - 2020 Dec 30
TI  - A Sentiment Analysis Approach to Predict an Individual's Awareness of the 
      Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.
LID - 10.3390/ijerph18010218 [doi]
LID - 218
AB  - 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 Naive 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.
FAU - Aljameel, Sumayh S
AU  - Aljameel SS
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Alabbad, Dina A
AU  - Alabbad DA
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Alzahrani, Norah A
AU  - Alzahrani NA
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Alqarni, Shouq M
AU  - Alqarni SM
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Alamoudi, Fatimah A
AU  - Alamoudi FA
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Babili, Lana M
AU  - Babili LM
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Aljaafary, Somiah K
AU  - Aljaafary SK
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
FAU - Alshamrani, Fatima M
AU  - Alshamrani FM
AD  - Department of Computer Science, College of Computer Science and Information 
      Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
LA  - eng
PT  - Journal Article
DEP - 20201230
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Bayes Theorem
MH  - COVID-19/*prevention & control
MH  - Disease Outbreaks/*prevention & control
MH  - *Health Knowledge, Attitudes, Practice
MH  - Humans
MH  - Public Health
MH  - Saudi Arabia/epidemiology
MH  - Support Vector Machine
PMC - PMC7795573
OTO - NOTNLM
OT  - Arabic sentiment analysis
OT  - K-nearest neighbor
OT  - N-gram
OT  - Twitter
OT  - machine learning
OT  - natural language processing
OT  - naive bayes
OT  - support vector machine
COIS- The authors declare no conflict of interest.
EDAT- 2021/01/06 06:00
MHDA- 2021/01/12 06:00
PMCR- 2021/01/01
CRDT- 2021/01/05 01:16
PHST- 2020/11/24 00:00 [received]
PHST- 2020/12/25 00:00 [revised]
PHST- 2020/12/27 00:00 [accepted]
PHST- 2021/01/05 01:16 [entrez]
PHST- 2021/01/06 06:00 [pubmed]
PHST- 2021/01/12 06:00 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - ijerph18010218 [pii]
AID - ijerph-18-00218 [pii]
AID - 10.3390/ijerph18010218 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2020 Dec 30;18(1):218. doi: 
      10.3390/ijerph18010218.