Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia

Abstract

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

“…Machine learning approaches used supervised classification algorithms to extract information regarding sentiment polarity. Classical machine learning models mainly included naïve Bayes, support vector machine, random forest, decision tree, and logistic regression [ 60 , 61 , 67 , 70 , 76 ]. Additional machine learning techniques for polarity classification were Microsoft Azure cognitive services, Amazon Web Services (AWS), and Baidu’s AipNLP [ 59 , 64 , 75 ].…”

“…These sentiment analysis studies showed that public sentiments were associated with real-time news, internet information, public health events, the number of COVID-19 cases, vaccine development, the pandemic, and announcements of political leaders or authorities [ 7 , 21 , 27 , 31 , 51 , 52 , 55 , 56 , 58 , 65 ]. Although public sentiments on COVID-19 vaccines varied significantly over time and geography [ 7 , 33 , 39 ], positive sentiments were more prevalent than negative ones regarding COVID-19 vaccines [ 7 , 19 , 20 , 22 , 25 , 29 , 33 , 41 - 44 , 46 , 47 , 51 , 57 , 60 , 62 , 65 , 70 , 72 , 76 , 78 , 84 , 85 ], with trust and anticipation being the predominant emotions [ 20 , 23 , 32 , 37 , 50 , 54 , 58 ]. However, some other studies found that negative sentiments overwhelmed positive ones, with fear being the dominant emotion [ 53 , 59 , 64 , 66 , 71 , 73 , 79 - 82 , 86 ].…”

“…Nineteen sentiment analysis studies were based on machine learning, and they trained machine learning classifiers by annotating tweets in the data set [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]. Machine learning approaches used supervised classification algorithms to extract information regarding sentiment polarity.…”

“…Seven studies employed hybrid methods combining lexicons and machine learning for polarity classification [7,[78][79][80][81][82][83]. Accuracy, precision, recall, and F1 score were typically used to evaluate the performance of classification models [61,[68][69][70]. In terms of model performance, 3 studies showed that LSTM outperformed other classifiers [68,70,78].…”

“…Accuracy, precision, recall, and F1 score were typically used to evaluate the performance of classification models [61,[68][69][70]. In terms of model performance, 3 studies showed that LSTM outperformed other classifiers [68,70,78].…”