Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has greatly impacted the normal life of people worldwide. One of the most noticeable impacts is the enforcement of social distancing to reduce the spread of the virus. The Ministry of Education in Saudi Arabia implemented social distancing measures by enforcing distance learning at all educational stages. This measure brought about new experiences and challenges to students, parents, and teachers. This research measures the acceptance rate of this way of learning by analysing people’s tweets regarding distance learning in Saudi Arabia. All the tweets analysed were written in Arabic and collected within the boundary of Saudi Arabia. They date back to the day that the distance learning announcement was made. The tweets were pre-processed, and labelled positive, or negative. Machine learning classifiers with different features and extraction techniques were then built to analyse the sentiment. The accuracy results for the different models were then compared. The best accuracy achieved (0.899) resulted from the Logistic regression classifier with unigram and Term Frequency-Inverse Document Frequency as a feature extraction approach. This model was then applied on a new unlabelled dataset and classified to different educational stages; results demonstrated generally positive opinions regarding distance learning for general education stages (kindergarten, intermediate, and high schools), and negative opinions for the university stage. Further analysis was applied to identify the main topics related to the positive and negative sentiment. This result can be used by the Ministry of Education to further improve the distance learning educational system.