在COVID-19大流行期间测量学生的压力、焦虑和抑郁

Pub Date : 2022-12-22 DOI:10.12694/scpe.v23i4.2012
Astha Singh, Divya Kumar
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引用次数: 0

摘要

在COVID-19大流行开始期间,世界各地都有关于健康问题的研究。研究人员开始发现这种病毒的影响。该病毒被发现是多功能的,因为它可以改变其性质并以人的肺部为目标。后来,由于这种病毒,世界各地发生了令人震惊的大屠杀。许多人失去了生命,但更多的人仍然忍受着不良的心理状态。研究人员开始对自然病毒进行研究,但对这次大流行的其他副作用进行的研究很少。在当代世界,一个重要的问题是COVID-19对普通人群心理状态的影响。这种副作用可能导致未来出现令人担忧的情况,可能导致更多的死亡病例。拟议的论文提出了一项关于检测由大流行引起的人们的压力和抑郁的研究。提出的方法是基于感知问卷法,通过人们的反应记录在文本的形式。COVID - 19受害者将被询问一系列问题,并记录他们的回答。该方法对他们的回答进行文本挖掘,其中还包括人们在社交网站上的反应。人们的反应的文本处理是由自然语言处理(NLP)完成的。NLP用于将纹理事实解释为有意义的片段,这些片段必须是机器可以理解的。将精细化后的数据转化为PSS(感知压力量表)比例因子,该比例因子取值范围为0 ~ 4,表示不同的压力水平。该系统利用人工智能,采用朴素贝叶斯分类器、k近邻(KNN)、决策树和随机森林算法来预测人的情绪状态。该系统还使用来自社交网站的数据进行测试。该模型成功地展示了这三种分类器对压力水平分为压力、焦虑和抑郁的比较研究。
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Gauging Stress, Anxiety, Depression in Student during COVID-19 Pandemic
During the beginning of COVID-19 pandemic, studies came across the world concerning with health issues. Researches began to find the repercussions of the virus. The virus was found to be versatile as it changes its nature and targets the lungs of a person. Later, it was seen an astonishing massacre around the world due to the virus. Many people have lost their life but many more people are still suffering with bad psychological state. Researchers began to research on the nature virus but very few researches were made on the other side-effects of this pandemic. One such crucial subject to attend in contemporary world is the effect of COVID-19 on psychological state in general population. This side-effect may lead to raise an alarming situation in future that could result in more death cases. The proposed paper presents a study on the detection of stress and depression in people caused by the pandemic. The proposed methodology is based on perceived questionnaire method through which people’s responses are recorded in the form of text. COVID victims have been interrogated against a set of questions and their responses are recorded. The methodology performs text mining of their responses that also include the people’s reaction from social networking sites. The text processing of people’s responses is done by natural language processing (NLP). NLP is used to interpret textural facts into meaningful segments that must be understandable to machine. The refined data has been transformed into PSS (perceived stress scale) scaling factor that ranges from 0 to 4 showing various level of stress. The proposed system utilized artificial intelligence in which naive Bayes classifier, K-nearest neighbor (KNN), Decision tree and Random forest algorithms are applied to predict the emotional state of a person. The proposed system also uses data from social networking site for testing purpose. The model successfully shows a comparative study of such three classifiers for the classification of stress level into stress, anxiety and depression.
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