Detecting Mental Distress through User's Social Media Activity

Users of social networking sites can approach their friends who are interested and expressing their thoughts, feelings, and sentiments through ideas, photographs, and videos. This opens the door to studying online information for user emotions and feelings in order to gain a better understanding of their emotions and attitudes when utilizing these online platforms. Depression may be dangerous to one's health, particularly if it is recurring and of moderate or severe degree. It can make the individual suffer a lot and make them perform poorly at job, school, and at home. Suicide is a possibility when depression is severe. It is one of the leading causes of death among those between 15 to 29 of age. Machine learning algorithms and Natural Language As the facts state that around 700,000 people in one year kill themselves. Processing will be employed in the proposed problem statement to detect if a person is going through mental distress. The main aim is to discover that commonality within the tweets that can help in identifying whether the individual is on the edge of mental distress so that there’s no delay is reaching out and helping the person who is suffering