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A Deep Learning Approach for Suicide Risk Assessment using Reddit

Shetty, Shrinidhi Chandrashekhar (2020) A Deep Learning Approach for Suicide Risk Assessment using Reddit. Masters thesis, Dublin, National College of Ireland.

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The advancement in technology has ironically connected the world and separated from each other. The world is drastically progressing in the technical aspect and exponentially dropped in terms of preserving composite mental health. The mental stress from the external environment leads to developing self-harm thoughts. Suicide is the most alarming trend in today’s life. It is necessary to build proactive systems to stop people from taking such drastic decisions to end their life. The research focuses on analyzing the pattern followed in stages of Suicide Risk- starting from Suicide Ideation, Suicide Indicator, Suicide Behaviour, and Suicide Attempt. The research begins with a comprehensive analysis of building a large labeled dataset from the sparse dataset available with a semisupervised learning approach to overcome the scarcity of data. The dataset is critically evaluated to model a multi-class text classifier and understand the factors and sentiments related to each of the target classes. The research builds models based on content analysis and deep neural networks to incorporate an assessment tool in online sites to monitor the suicide risk patterns. The results can be a compiled report for mental health care workers to understand the underlying pattern.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > RA Public aspects of medicine > RA790 Mental Health
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 11:00
Last Modified: 21 Jan 2021 11:00

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