Management and Plan of Undergraduates’ Mental Health Based on Keyword Extraction

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.

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

“…Research related to keyword extraction from text includes extracting keywords from the "sina news" corpus with the number of keywords from one document including at least 3 keywords and at most 6 keywords contained in [21]. Other research extracts keywords from questionnaire documents with at least 1 keyword and a maximum of 10 keywords from each document [22]. Based on the results of these two studies, it can be concluded that the least number of keywords that can be extracted is 1 keyword and at most 10 keywords.…”