ABSTRACT
Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy. © 2022 IEEE.