Depression and Suicide Prediction Using Natural Language Processing and Machine Learning

Depression has always been one of the prominent concerns of mental health worldwide. In the worst-case scenario, someone suffering from depression may lead to drastic measures such as suicide. According to the World Health Organization, depression and anxiety affect one out of every five people worldwide, costing trillions of dollars each year. In the COVID-19 pandemic, the situation has worsened alarmingly as more people suffer from depression. It has become essential, more than ever, to  maintain the mental health profiles of our people and to predict any unfortunate event. Depression can be prevented and treated at a very early stage and a low cost, given early detection and identification of the causes. With advancements in machine and deep learning models, it has become possible to identify such behaviour through social interactions such as posts, tweets, and comments. This paper aims to detect user behaviour that can conclude whether a person is suffering from depression and suicidal tendencies based on the user’s social media tweets. The research work proposes a classifier with a hybrid technique in preprocessing using Natural Language Processing (NLP) and machine learning techniques with an accuracy of 75% to identify such traits in a person through his/her tweets.