Suicide ideation detection and response system for textual social media posts

Suicide is the fourth most common cause of mortality among youths. With t emergence of digital technology , media platforms as a "safe space " to i he ndividuals are increasingly using social express their suicidal tendencies this project focu supports real. As such, ses on developing a co...

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Main Author: Lim, Yan Qian
Format: Final Year Project / Dissertation / Thesis
Published: 2022
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Online Access:http://eprints.utar.edu.my/5015/1/2005778_LIM_YAN_QIAN.pdf
http://eprints.utar.edu.my/5015/
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spelling my-utar-eprints.50152022-12-26T14:06:27Z Suicide ideation detection and response system for textual social media posts Lim, Yan Qian QA76 Computer software Suicide is the fourth most common cause of mortality among youths. With t emergence of digital technology , media platforms as a "safe space " to i he ndividuals are increasingly using social express their suicidal tendencies this project focu supports real. As such, ses on developing a comprehensive web application that time classification of tweets into three suicide risk categories and triggers a tailored crisis response that targets specific suicide risk levels. To that end, this project covers the en dtoend activities from model development, which uses Natural Language Processing and feature extraction techniques to improve the model’s classification performance, to its deployment on Flask web application framework for realtime monitoring and detect ion of tweets, and finally the initiation of proactive responses tailored to specific suicide risk levels. The methodology adopted for this project is Scrum methodology, which runs on 3 sprints. Results showed that the approach used significantly improved the classification performance when benchmarked with existing works. The model was integrated into the web system developed in this project and tested with random tweet samples of varying suicide risk. Overall, it was shown that the model maintained high p able to proactively erformance results and the system was trigger the correct response that addressed each suicide risk, which proves the efficacy of the model in realtime environment. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5015/1/2005778_LIM_YAN_QIAN.pdf Lim, Yan Qian (2022) Suicide ideation detection and response system for textual social media posts. Final Year Project, UTAR. http://eprints.utar.edu.my/5015/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic QA76 Computer software
spellingShingle QA76 Computer software
Lim, Yan Qian
Suicide ideation detection and response system for textual social media posts
description Suicide is the fourth most common cause of mortality among youths. With t emergence of digital technology , media platforms as a "safe space " to i he ndividuals are increasingly using social express their suicidal tendencies this project focu supports real. As such, ses on developing a comprehensive web application that time classification of tweets into three suicide risk categories and triggers a tailored crisis response that targets specific suicide risk levels. To that end, this project covers the en dtoend activities from model development, which uses Natural Language Processing and feature extraction techniques to improve the model’s classification performance, to its deployment on Flask web application framework for realtime monitoring and detect ion of tweets, and finally the initiation of proactive responses tailored to specific suicide risk levels. The methodology adopted for this project is Scrum methodology, which runs on 3 sprints. Results showed that the approach used significantly improved the classification performance when benchmarked with existing works. The model was integrated into the web system developed in this project and tested with random tweet samples of varying suicide risk. Overall, it was shown that the model maintained high p able to proactively erformance results and the system was trigger the correct response that addressed each suicide risk, which proves the efficacy of the model in realtime environment.
format Final Year Project / Dissertation / Thesis
author Lim, Yan Qian
author_facet Lim, Yan Qian
author_sort Lim, Yan Qian
title Suicide ideation detection and response system for textual social media posts
title_short Suicide ideation detection and response system for textual social media posts
title_full Suicide ideation detection and response system for textual social media posts
title_fullStr Suicide ideation detection and response system for textual social media posts
title_full_unstemmed Suicide ideation detection and response system for textual social media posts
title_sort suicide ideation detection and response system for textual social media posts
publishDate 2022
url http://eprints.utar.edu.my/5015/1/2005778_LIM_YAN_QIAN.pdf
http://eprints.utar.edu.my/5015/
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score 13.211869