Designing of Latent Dirichlet Allocation Based Prediction Model to Detect Midlife Crisis of Losing Jobs due to Prolonged Lockdown for COVID-19

The sudden outbreak of the virus COVID-19 has created a pandemic situation worldwide. Humankind has not experienced such a danger caused by this disease in the past hundred years. Apart from all the health issues, the pandemic has created an immense impact on social life, economics, mental peace, and all aspects of human life. Prolonged quarantine is creating uncertainties; death tolls are creating fear. According to the World Health Organization, this public health emergency is likely to create anxiety, loneliness, depression, fear of losing jobs, being economically unstable, and committing suicide. In our present discussion, we prepare a statistical record using data collected from all over the world to find the intensity of mental disorder caused by this pandemic. Now we aim at finding the polarity of the specified term used by social media users. We aim to formulate a highly efficient mechanism that will detect depressive sentences more accurately. In our work, we try to formulate an optimal mechanism implementing the Latent Dirichlet Allocation approach to modify our findings and prove through a comparative study that depression affects the highest among people age 40−50. We experience that this age group is highly devastated in fear of losing jobs because of to this pandemic. The standard psychiatric symptom of lack of self-dignity and self-confidence that can happen to a human at the middle age is proliferated due to extended lockdown and its after effects. There is much research in sentiment analysis, which shows us the impact of COVID-19 in recent days. Surprisingly, recognizing symptoms of the midlife crisis in the pandemic situation of COVID-19 is yet to achieve.