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.
