Suicidal Ideation Detection: Application of Machine Learning Techniques on Twitter Data

The World Wide Web, particularly Twitter, and
online social networks have expanded the network connecting
people, allowing for the rapid dissemination of information to
large numbers of people. There are several instances of this kind
of online collaborative contagion, one of which is the development
of self-destructive ideas in social media sites like Twitter, which
has caused alarm. In this investigation, the implications and
findings of several machine classifiers that were applied to the
point order of tweets and terms connected to suicide are
discussed. The classifier can distinguish between more stressful
information, such as suicidal creativity, other suicide-related
topics, in-depth suicide-related facts, loyalty, campaign, and
support. A simple classifier utilizing emotional, lexical,
psychological, and structural characteristics from Twitter is used
to link and identify allusions to suicide. This procedure makes use
of clustering, bracketing, association rules, NLP (natural
language processing), and numerous machine-learning
techniques. This research study explores the restrictions or
difficulties in this field and serve as a guide for future research.