Automation of depression detection in texts to identify possible cases during COVID-19 pandemic

The COVID-19 pandemic was more than a medical problem; it also caused social problems such as unemployment, business closure, and delivery services collapse; also, there is a human factor that was seriously damaged, mental health. Since secondary human activities such as work and school were transformed from physical to virtual modalities, people started to present problems related to their emotions and the lack of contact with other people. From one day to the next, human interactions were avoided in trying to preserve people's health, but for mental health, this was not the case. It was observed that throughout 2020 major depressive disorders as well as anxiety disorders increased due to the combined challenge of changing the life routines and the fear of being infected. In this chapter, we analyze the evolution of the research on depression and anxiety done during and after the pandemic. Also a natural language processing technique is implemented to identify depression in short texts of everyday life written by people, with a view to automate depression detection in such text and to refer possible cases to mental health experts.