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Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
Content Provider | MDPI |
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Author | Jojoa, Mario Garcia-Zapirain, Begonya Gonzalez, Marino J. Perez-Villa, Bernardo Urizar, Elena Ponce, Sara Tobar-Blandon, Maria Fernanda |
Copyright Year | 2022 |
Description | 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. |
Starting Page | 5705 |
e-ISSN | 16604601 |
DOI | 10.3390/ijerph19095705 |
Journal | International Journal of Environmental Research and Public Health |
Issue Number | 9 |
Volume Number | 19 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-05-07 |
Access Restriction | Open |
Subject Keyword | International Journal of Environmental Research and Public Health Information and Library Science Covid-19 University Student Socio-demographic Factors Satisfaction Perception Online Learning Mental Health Habits Institutions Continents Natural Language Processing Swivel Embedding Word Cloud |
Content Type | Text |
Resource Type | Article |