Depression Detection During the Covid 19 Pandemic by Machine Learning Techniques

Arora, S.; Malik, A.; Khurana, P.; Batra, I.

Coronavirus disease 2019 is a global pandemic caused by the (SARS-CoV-2) Severe Acute Respiratory Syndrome. During the outbreak of an infectious disease, the population’s psychological reactions play a crucial role in affecting both the spread of the disease and the occurrence of emotional distress. It is recognized that there are number of techniques and tools which are used to detect the depression. To assess whether the individual is depressed or not, machine learning techniques are used. The course of action is based on texts, images, videos and emoticons etc. Among different techniques, it is analyzed that none of the technique is using the probability method to detect the depression. In probability method, one can create a database, which is a collection of words related to emotions like happiness, sadness and anger etc., The probability would be set for all such words in the database and then it will be calculated by various techniques which shows the level of depression of a person. This paper summarized the findings on the identification of depressive mood disorders, using emotion analysis methods and techniques. The author focused on research that identifies irregular activity patterns on social networks automatically. The studies selected used the classic off-the-shelf classifiers to evaluate the knowledge available for lexicon use. To resolve this problem, the web application will be developed which will perform sentiment analysis with the help of classification function which recognizes the ratio of depressive and non-depressive thoughts. © 2021, Springer Nature Singapore Pte Ltd.