preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202111.0070.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 November 2021 / Approved: 3 November 2021 / Online: 3 November 2021 (09:06:22 CET)

The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student 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.

COVID-19; university student; socio-demographic factors; satisfaction; perception; online learning; mental health; habits; institutions; continents; Natural Language processing; Swivel embedding; Words Cloud.

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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