The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to agreat extent. Besides direct physical and economic threats, the pandemic also indirectly impactpeople’s mental health conditions, which can be overwhelming but difficult to measure. Theproblem may come from various reasons such as unemployment status, stay-at-home policy, fearfor the virus, and so forth. In this work, we focus on applying natural language processing (NLP)techniques to analyze tweets in terms of mental health. We applied deep learning models that classify each tweet into the following emotions: anger, anticipation, disgust, fear, joy, sadness, surpriseand trust. Furthermore, we propose and compare two methods tofind out the reasons that are causing sadness and fear. Two case studies show that the public have different emotion trend about specific topics, for example, lock down and masks.
We published our initial results and detailed analysis at https://arxiv.org/pdf/2004.10899.pdf.
More details please visit https://www.covid19analytics.org/project-details/emotion.