Authors:
Ravi Sharma
1
;
Sri Divya Pagadala
2
;
Pratool Bharti
2
;
Sriram Chellappan
1
;
Trine Schmidt
3
and
Raj Goyal
3
Affiliations:
1
Department of Computer Science and Engineering, University of South Florida, Tampa, FL, U.S.A.
;
2
Department of Computer Science, Northern Illinois University, DeKalb, IL, U.S.A.
;
3
Ajivar LLC, Tarpon Springs, FL, U.S.A.
Keyword(s):
COVID-19, Emotion, Mental Health, Natural Language Processing, Semantic Search.
Abstract:
In this paper, we report experimental results on assessing the impact of COVID-19 on college students by processing free-form texts generated by them. By free-form texts, we mean textual entries posted by college students (enrolled in a four year US college) via an app specifically designed to assess and improve their mental health. Using a dataset comprising of more than 9000 textual entries from 1451 students collected over four months (split between pre and post COVID-19), and established NLP techniques, a) we assess how topics of most interest to student change between pre and post COVID-19, and b) we assess the sentiments that students exhibit in each topic between pre and post COVID-19. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We also found that across all topics, negative sentiment among students post COVID-19 was much higher compared to pre-COVID-19. We expect our study
to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.
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