A Text Mining Analysis of Back-To-School Discussion During COVID-19
Junhe Yang, Juncheng Ding, University of North Texas, United States
Journal of Interactive Learning Research Volume 32, Number 2, ISSN 1093-023X Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
Preparing for back-to-school is essential, and it is also complicated because it involves different groups of people, and they have different needs. Pandemics, like COVID-19, make the situation even more difficult. Better preparation during a pandemic needs a comprehensive understanding of the situation. However, few studies have provided such information, especially during a pandemic. Furthermore, traditional research methods can hardly address the problems. This paper used text mining approaches to interpret open Twitter discussions on back-to-school preparation during COVID-19 and generated an inclusive insight into the preparation. Specifically, we collected 26219 related tweets and analyzed the user profile descriptions to determine who was involved in the preparation. We also examined the most frequently retweeted tweets and conducted a topic modeling analysis to find people’s concerns. Our research provides a holistic view of preparation for back-to-school during the pandemic, and it could help better prepare for back-to-school in the future pandemic.
Yang, J. & Ding, J. (2021). A Text Mining Analysis of Back-To-School Discussion During COVID-19. Journal of Interactive Learning Research, 32(2), 147-167. Waynesville, NC: Association for the Advancement of Computing in Education (AACE).
© 2021 Association for the Advancement of Computing in Education (AACE)