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

In the recent times, intensifying struggles for sustenance and education, have sharply escalated competitive tensions among individuals, leaving 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 they might otherwise keep to themselves. This research focuses on analyzing adolescents' Twitter posts for detection of depression. Textual data will be harvested from these posts and transformed into input suitable for machine learning algorithms. Different forms of machine learning algorithms are going to be experimented upon, using and not using NLP— Natural Language Processing, to design a system for the recognition of patterns of depression in individuals.