The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.
Keywords: COVID-19; Swivel embedding; continents; habits; institutions; mental health; natural language processing; online learning; perception; satisfaction; socio-demographic factors; university student; word cloud.