Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques

Mario Jojoa, Begonya Garcia-Zapirain, Marino J. Gonzalez, Bernardo Perez-Villa, Elena Urizar, Sara Ponce and Maria Fernanda Tobar-Blandon
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Mario Jojoa: Department of Computer Science, Engineering Faculty, Electronics and Telecommunications University of Deusto, 48014 Bilbao, Spain
Begonya Garcia-Zapirain: Department of Computer Science, Engineering Faculty, Electronics and Telecommunications University of Deusto, 48014 Bilbao, Spain
Marino J. Gonzalez: Unit of Public Policy, Simon Bolivar University, Caracas 89000, Venezuela
Bernardo Perez-Villa: Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, FL 33331, USA
Elena Urizar: Deusto Business School Health, University of Deusto, 48014 Bilbao, Spain
Sara Ponce: International Research Projects Office (IRPO), University of Deusto, 48014 Bilbao, Spain
Maria Fernanda Tobar-Blandon: Public Health School, Universidad del Valle, Cali 76001, Colombia

IJERPH, 2022, vol. 19, issue 9, 1-20

Abstract: 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; university student; socio-demographic factors; satisfaction; perception; online learning; mental health; habits; institutions; continents; natural language processing; Swivel embedding; word cloud (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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