Gulf Countries’ Citizens’ Acceptance of COVID-19 Vaccines—A Machine Learning Approach

Mathematics

Hafiz Tayyab Rauf¹  Talha Meraj²  Ofonime Dominic Okon³  Husam M. Alawadh⁴  Amerah Alabrah⁵ 

[1] Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK;Department of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt 47040, Pakistan;Department of Electrical/Electronics & Computer Engineering, Faculty of Engineering, University of Uyo, Uyo 520103, Nigeria;Department of English Language and Translation, College of Languages and Translation, King Saud University, Riyadh 11451, Saudi Arabia;Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;

关键词: COVID-19;    long short-term memory;    deep learning;    machine learning;    VADER;    discourse;   

DOI  :  10.3390/math10030467

来源: DOAJ

【 摘 要 】

The COVID-19 pandemic created a global emergency in many sectors. The spread of the disease can be subdued through timely vaccination. The COVID-19 vaccination process in various countries is ongoing and is slowing down due to multiple factors. Many studies on European countries and the USA have been conducted and have highlighted the public’s concern that over-vaccination results in slowing the vaccination rate. Similarly, we analyzed a collection of data from the gulf countries’ citizens’ COVID-19 vaccine-related discourse shared on social media websites, mainly via Twitter. The people’s feedback regarding different types of vaccines needs to be considered to increase the vaccination process. In this paper, the concerns of Gulf countries’ people are highlighted to lessen the vaccine hesitancy. The proposed approach emphasizes the Gulf region-specific concerns related to COVID-19 vaccination accurately using machine learning (ML)-based methods. The collected data were filtered and tokenized to analyze the sentiments extracted using three different methods: Ratio, TextBlob, and VADER methods. The sentiment-scored data were classified into positive and negative tweeted data using a proposed LSTM method. Subsequently, to obtain more confidence in classification, the in-depth features from the proposed LSTM were extracted and given to four different ML classifiers. The ratio, TextBlob, and VADER sentiment scores were separately provided to LSTM and four machine learning classifiers. The VADER sentiment scores had the best classification results using fine-KNN and Ensemble boost with 94.01% classification accuracy. Given the improved accuracy, the proposed scheme is robust and confident in classifying and determining sentiments in Twitter discourse.

【 授权许可】

Unknown