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Suicidal Analysis on Social Networks Using Machine Learning
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Author(s): Kanojia Sindhuben Babulal (Central University of Jharkhand, India)and Bashu Kumar Nayak (Central University of Jharkhand, India)
Copyright: 2023
Pages: 18
Source title:
The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications
Source Author(s)/Editor(s): Rajiv Pandey (Amity University, Lucknow, India), Amrit Gupta (MRH, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India)and Agnivesh Pandey (D.A-V. College, Chhatrapati Shahu Ji Maharaj University, Kanpur, India)
DOI: 10.4018/978-1-6684-3533-5.ch012
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Abstract
Suicides are the most critical issues in the present time. Early detection and prevention can assure the safety for the people's lives. As the technology increases rapidly, we are moving towards online channels to express our suicidal thoughts. In the chapter, the authors deal with suicidal ideation through the user generated post on different platforms like Twitter, Facebook, Reddit, Suicide Watch, etc. Analyzing the text, they enrich the knowledge and that can be used as an indicator for suicidal thoughts. To detect suicidal thoughts, they use text processing using NLP, and some features are generated that can be classified using different classifiers like random forest, SVM, naïve bayes, etc., and some neural network models like CNN, LSTM, BERT, etc. are also used for final prediction of suicidal or non-suicidal thoughts. In this chapter, the authors use Distill Bert model for predicting the results and also improve the accuracy by changing the hyperparameters. Here, they summarize the existing work's limitations and discuss future research directions.
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