Depression and Disclosure Behavior via Social Media using CNN and NLP

Abstract

Abstract: Nowadays, mental stress has become a danger to human progress. As life moves fast, more and more people feel uncomfortable. It is not always good to understand customer concern early and keep customers safe. Thanks to informal digital communication, people take the time to share some of their experiences and talk to friends through digital systems and media companies, making it possible to have online relationships that involve anxiety identity. In our paper, we found that in web-based omnimedia the user's stress state is related to the stress of his friends, and I use continuous data from appropriate levels to avoid always acknowledging the client's stress and relationship. Discussion In our system, we see that customer's kingdom anxiety is related to their friends' digital kingship anxiety, and we use real advanced levels of important certification information to understand user stress and business relationships. together. As a shift in thinking, I started by unpacking many of the tensions around print, visual, and social, and proposed a format in which we could experiment with Facebook ads using the form of a neural network. We can understand if the customer is upset. After asking whether the patient is afraid or not, the assistant's opinion of the clinic should be checked, and the administrator can send a safe list to the user so that he can strengthen and shine in daily life. -CNN, classification, depression, machine learning;