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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.