Determining the effects of the COVID-19 pandemic process on the education via machine learning methods

The global COVID-19 pandemic in 2020 has led to catastrophic economic and social disruption. The pandemic has affected almost every aspect of our lives, including health, food, business organizations, and education. An essential shift in the higher education field has been occurred with the digitalization of instruction. In attempt to combat the pandemic, several higher education institutions throughout the world have begun to offer undergraduate and graduate courses online, either asynchronously or synchronously. During this period, people make considerable use of social media to gain news, information, social connections, and support. As a result, the immense quantity of electronic text documents has been shared on the Web related to COVID-19. In this study, we present a deep learning-based sentiment analysis approach to analyze the impact of COVID-19 pandemic on the higher education. In this regard, the predictive performance of conventional machine learning algorithms (Support Vector Machines, Naïve Bayes, Logistic Regression, and Random Forest) and deep neural networks (Convolutional Neural Network, Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit). In addition, the empirical results obtained by the bidirectional encoder representations from transformers (BERT) has been evaluated. The comprehensive empirical results with different text representation models and classification algorithms indicate that deep neural networks can yield promising results for the task of analyzing the impact of COVID-19 related text documents on the higher education.