论文标题
通过自动处理自由形式的文本评估COVID-19对大学生的影响
Assessing COVID-19 Impacts on College Students via Automated Processing of Free-form Text
论文作者
论文摘要
在本文中，我们报告了通过处理由他们产生的自由形式文本来评估Covid-19对大学生的影响的实验结果。通过自由形式的文本，我们的意思是通过专门设计和改善其心理健康的应用程序，由大学生（已入学四年的美国学院入学）发表文本条目。使用一个数据集，该数据集由1451名学生收集的9000多个文本条目（在COVID-19-19中分配），并建立了NLP技术，a）a）我们评估了学生在Covid-19和post post tof tof tof-n和b之间最大程度地改变的主题，以及b）我们评估了学生在每种主题中的观点，即在pre和post covid post covid-n之间展示的观点。我们的分析表明，教育之类的主题对Covid-19之后的学生而变得明显降低，而健康变得更加趋势。我们还发现，在所有主题中，与19岁前的19岁前学生的负面情绪相比要高得多。我们预计我们的研究会影响多个光谱的高等教育决策者，包括大学管理人员，教师，父母和心理健康顾问。
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.
