A Comparative Analysis of Depression Detection with and without Natural Language Processing

Abstract

In the recent times, intensifying struggles for sustenance and education, have sharply escalated competitive tensions among individuals. This heightened competition has left many students especially the adolescents in prolonged states of stress and apprehension, due to the lack of proper guidance, trauma or other aspects leading to a significant surge in mental health issues. The recent advancement in social media like Instagram, Facebook, Twitter, etc. has made the communication easy but also has become something that is offering these teenagers a fresh opportunity or method to release or express their emotions which many of their age group tend to keep to oneself. This could be a platform, activity, or resource that allows them to openly share and manage their feelings, thus serving as a beneficial outlet for their emotional well-being. They tend to share their lives through the social network and interact with their friends online with whom they feel at ease to share their thoughts. All this has greatly helped in detecting the depressed individuals by analysing their social network data. Here this study involves text-based analysis of the Twitter posts by the adolescent users to detect depression in them. This starts with collection of textual data from the Twitter posts by the teenagers and then converting it into input data that can be used with the Machine Learning algorithms. Several kinds of machine learning algorithms will be experimented with and without using NLP(Natural Language Processing). This depression recognition scheme refers to a method or system designed to identify signs or patterns associated with depression in individuals by utilizing data from Twitter.