  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.
