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.

Sustainable Participatory Governance: Data-Driven Discovery of Parameters for Planning Online and In-Class Education in Saudi Arabia During COVID-19

“…Some have focused on studying students and their attitudes while others have focused on teachers and other stakeholders. These include the view toward online learning of people in Saudi Arabia (Aljabri et al, 2021), higher education students in Pakistan (Adnan and Anwar, 2020), high school students in Indonesia (Bestiantono et al, 2020), primary school teachers (Rasmitadila et al, 2020), and K-12 teachers (Harron and Liu, 2020).…”

Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data

“…A more recent study by Aljabri et al [29] applied sentiment analysis using machinelearning techniques to investigate people's attitudes about the topic of distance learning in Saudi Arabia using Twitter data. They collected approximately 14,000 tweets to use as a sample in building a number of classical machine-learning algorithms, including SVM, NB, k-nearest neighbor (KNN), logistic regression (LR), and XGBoost (XGB).…”

Tweet topics and sentiments relating to distance learning among Italian Twitter users

“…Twitter allows users to express and spread opinions, thoughts and emotions as concisely and quickly as possible. Therefore, researchers have often preferred to analyze user comments on Twitter to immediately uncover insights about social issues during the coronavirus pandemic (e.g., conspiracy theories 7 , why people oppose wearing a mask 8 , experiences in health care 9 , and vaccinations 10 ) or distance learning 11 – 13 .…”