摘要
正值于青少年时期的大学生很容易受到各种外界因素的影响,导致心理情绪波动较大。特别是进入21世纪以来的大学生大多都是独生子女,通常具有感情色彩丰富、心理较脆弱的特点,如果长期处于一种负面情绪的状态则很有可能会引发一些极端的不良事件,因此维护大学生的心理健康成为高校教师的重点工作。而传统的心理测评往往容易受到主观条件的影响不能准确和及时地反映当前学生的心理问题,本文结合当前互联网大数据时代的特征,提出一种利用模糊聚类算法对大学生在微博等网络社交平台的文本状态进行情感分析的方法,旨在能够及时有效地发现学生的心理健康问题避免不良的影响。
College students who are in adolescence are very susceptible to various external factors, resulting in greater fluctuations in psychological mood. In particular, most of the college students who have entered the new century are only children. They are usually characterized by rich emotions and psychological weakness. If they are in a state of negative emotion for a long time, they are likely to cause some extreme adverse events. Mental health has become a key task for college teachers. However, the traditional psychological assessment is often susceptible to subjective conditions, which can not accurately and timely reflect the current students’ psychological problems. This paper combines the characteristics of the current Internet big data era, and proposes a fuzzy clustering algorithm for college students to socialize on Weibo and other networks. The method of emotional analysis of the textual state of the platform aims to be able to timely and effectively discover the mental health problems of students to avoid adverse effects.
作者
仲伟伟
刘丽萍
汪方正
ZHONG Wei-wei;LIU Li-ping;WANG Fang-zheng(Jiangsu Vocational College of Medicine,Yancheng 224005,China)
出处
《电脑知识与技术》
2019年第10期226-228,233,共4页
Computer Knowledge and Technology
基金
江苏省社会科学基金项目“大数据背景下智慧校园建设的策略与实践研究”（15JYC001）
关键词
模糊理论
聚类算法
大数据
情感分析
人工智能
Fuzzy theory
Clustering algorithm
Big data
Sentiment analysis
Artificial intelligence
