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Sentiment Analysis of COVID-19 Vaccination in Saudi Arabia


Sawsan Alowa, Lama Alzahrani, Noura Alhakbani, and Hend Alrasheed


Vol. 23  No. 2  pp. 13-30


Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people’s perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people’s reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used ?support vector machine (SVM), na?ve Bayes (NB), logistic regression (LOGR), and random forest (RF)?in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.


COVID-19 vaccine; sentiment analysis; BiLSTM; polarity; machine learning; SMOTE; oversampling