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      A crucial Psychological Analysis of Mental Anxieties of Job-seekers and Working Classes in Society

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      ScienceOpen Preprints

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      Mental Anxiety, Deep Learning, CNN, LSTM, Job-Seekers, Employees

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          Abstract

          Corona Virus Infectious Disease (COVID-19) appeared on Earth in December, 2019. This life-threatening disease is taking away numerous human lives at an exponential rate throughout the world. The World Health Organization (WHO) declared a presently appeared (epidemiological) situation due to this infectious disease as Pandemic. This disease not only threatens public health but also socio-economic conditions are also negatively affected by the current scenario. The working environment is devastated by the global virus pandemic situation. In this paper, different classes of peoples such as job-seekers, current employees and their current status are to be considered. It presents concentrations to assess different perspectives of mental conditions of a specific class of peoples. During the pandemic situation, job-seekers feel insecure regarding their placement since campus interviews either online or offline have not occurred due to COVID-19. In addition, currently employed workers are also mentally annoyed about their job-loss due to the financial scenario of the industries are not in a stable condition. In these cases, stress, depression and anxiety are quite evident. To carry out the research for both of these aforementioned cases, 500 students and (specify no. of) employees are surveyed for the period from April 2020 to July, 2020. The collected data are focused on peoples of Kolkata, West Bengal, India. This research work uses Machine Learning (ML) algorithm to assess mental well-being of job-seekers as well as currently placed workers. A hybrid model is presented in this paper that detects mental health status of job-seekers and existing employees. The hybrid model has a mixture of Deep Learning (DL) technique with Convolutional Neural Network. It is a predictive model consisting of two major components such as, CNN and Long-Short term memory (LSTM). LSTM is a variant of Recurrent Neural Network (RNN). This model is applied on the collected data for finding the status of mental anxieties of both classes. Experimental results imply that mental well-beings of job-seekers and presently working employees are predicted with an accuracy of 93.22% and 89.69% respectively. It concludes that the working peoples are more affected than those who may be a working class in near future.

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          Author and article information

          Journal
          ScienceOpen Preprints
          ScienceOpen
          6 October 2020
          Affiliations
          [1 ] The Bhawanipur Education Society College
          Article
          10.14293/S2199-1006.1.SOR-.PPDTYG3.v1

          This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

          Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

          Computer science

          Mental Anxiety, Employees, Job-Seekers, LSTM, CNN, Deep Learning

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