Tweeter Suicidal Notes Identification Using Adversarial Deep Theory

Abstract

Depression is a common mental illness that can interfere with daily activities and productivity. Suicidal thoughts or attempts may result as a result. In today's society, suicide is a major issue. Suicide attempts should be detected and prevented early on in order to preserve people's lives. Natural Language Processing (NLP) and machine learning techniques were used to construct the platform, which was designed to interpret conversations. The proposed two-stage platform would evaluate conversation and categorize associated sentiments into four categories: “happy”, “neutral”, “depressive”, and “suicidal”. The first step of intent recognition would examine conversations and categorize associated sentiments into two categories: "YES" and "NO". We show how social media data and suicide notes can be used to identify people who are in danger of committing suicide. Suicide notes are usually written in letters and posted on websites, and they're also captured in audio and video. Suicide notes can be used as study material in NLP. This article comprehensively introduces and explores methods and algorithms from a variety of disciplines. We also investigate the implications of using the same gear and thus the security implications. For knowledge-aware suicide risk assessment, current research incorporated external knowledge utilizing knowledge bases and suicide ontology. Even this surgical technique however is actually currently there only available as well for state intervention among users whom had "checked out" either study and counseling. Still, it allows for scalable suicide risk screening, potentially identifying many people who are at risk before they engage with services in the health infrastructure. Finally, we review considering the current state of affairs work's shortcomings and offer some recommendations for further research.