Abstract

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Abstract

Mental health issues are alarmingly on the rise among undergraduates, which have gradually become the focus of social attention. With the emergence of some abnormal events such as more and more undergraduates’ suspension, and even suicide due to mental health issues, the social attention to undergraduates’ mental health has reached a climax. According to the questionnaire of undergraduates’ mental health issues, this paper uses keyword extraction to analyze the management and plan of undergraduates’ mental health. Based on the classical TextRank algorithm, this paper proposes an improved TextRank algorithm based on upper approximation rough data-deduction. The experimental results show that the accurate rate, recall rate, and F1 of proposed algorithm have been significantly improved, and the experimental results also demonstrate that the proposed algorithm has good performance in running time and physical memory occupation.

۱٫ Introduction

The mental health and wellbeing of undergraduates have deteriorated over the last decade. Before the COVID-19 pandemic, higher education was facing a “mental health crisis” [۱, ۲]. The rapid onset of the COVID-19 pandemic has introduced countless additional stressors, and faculty concern over student wellbeing has increased. Over the past ten or twenty years, the depression has increased from about 25% of undergraduates in 2010 to almost 30% of undergraduates in 2020, and the anxiety of undergraduates has increased from 22% in 2014 to 31% in 2020. Suicidal ideation of undergraduates has increased from 6% in 2010 to 11% in 2020 [3]. The frequency of mental health management organization in undergraduates varies from university to university. Definitely influence of the pandemic on mental health concerns within undergraduates is a big concern. The pandemic has affected the economic development of many countries, and the cooperation of relevant countries on the pandemic has also led to conflicts. The widespread public reports on the Internet and the media have made simple and inexperienced undergraduates unable to distinguish. So, the management and plan of undergraduates’ mental health are important under COVID-19 pandemic [4, 5].

Keywords are words that express the central content of a document. Keywords from a document can accurately describe the document’s content and facilitate fast information processing. There are two main types of keyword extraction algorithms, which are unsupervised keyword extraction method and supervised keyword extraction method [6–۸]. Unsupervised keyword extraction method does not need manually labeled corpus, but it uses some methods to find important words in the text as keywords for keyword extraction. In unsupervised keyword extraction method, candidate words are firstly extracted, and then each candidate word is scored, so top-K candidate words with the highest score are output as keywords. According to different ranking strategies, there are different algorithms such as term frequency-inverse document frequency (TF-IDF), TextRank, and latent Dirichlet allocation (LDA). The supervised keyword extraction method regards the keyword extraction process as a binary classification problem. At first, the candidate words are extracted, and then each candidate word is labeled, so the keyword extraction classifier is trained. When a new document is coming, all candidate words are extracted, and then the trained keyword extraction classifier is used to classify each candidate word. Finally, the candidate words labeled as keywords are used as keywords [9, 10].