Sentiment Analysis of The Covid-19 Vaccine For Arabic Tweets Using Machine Learning
Ruba Alhejaili, Enas S. Alhazmi, Abdullah Alsaeedi, Wael M. S. Yafooz · 2021
With exposure through social media, everyone has the freedom to publish their opinions and feelings with no restrictions. These opinions and feelings may be about individual or general trends of society in general. These feeling that are related to topics or general trends must be analysed. The purpose of this paper is to analyze sentiment for tweets posted about the COVID-19 vaccine. For this purpose, a dataset focusing on the tweets which were recorded between the February 19th to the 20th of March 2021 related to the COVID-19 vaccine was collected. Several classic machine learning methods were used, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), AdaBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB) were utilized, the preprocessing and annotation process steps on the proposed dataset. The TF -IDF feature extraction method where the dataset is trained based on unigram, bigram, and trigram features was applied. The best results were achieved through the bigram, where the LR model achieved the highest percentage in the accuracy with a value of 87.0%.