Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias

People are more comfortable sharing their thoughts on social media rather than someone in person. Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. Individuals who suffer from suicidal ideation frequently expre...

Full description

Saved in:

Bibliographic Details
Main Author:	Alias, Annasuha Atie Atirah
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Human behavior Behaviorism Neobehaviorism Behavioral psychology
Online Access:	https://ir.uitm.edu.my/id/eprint/88979/1/88979.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.88979
record_format	uketd_dc
spelling	my-uitm-ir.889792024-03-19T07:07:22Z Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias 2023 Alias, Annasuha Atie Atirah Human behavior. Behaviorism. Neobehaviorism. Behavioral psychology People are more comfortable sharing their thoughts on social media rather than someone in person. Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. It is essential to develop a machine learning system for early detection of suicidal ideation by analyzing his or her posts on social media. The prevailing methods of identifying and addressing suicidal content on social media exhibit limitations. Relying on human experts for identification is both costly and time-consuming. Additionally, the vast volume of data on platforms like Twitter makes manual outreach impractical. Existing machine learning models for sentiment analysis and suicide prediction lack a comprehensive user-friendliness system for the end users. Thus, this project aims to design, develop, and evaluate web-based application utilizing sentiment analysis, specifically employing the Naïve Bayes algorithm, to identify and analyze suicidal ideation within Twitter posts. By harnessing natural language processing techniques and data visualization tools, the project seeks to provide user-friendly solution for early detection and prevention of suicidal intentions expressed on social media. The goal is to empower suicide prevention organizations and concerned individuals with an efficient and accessible means of taking timely actions toward individuals at risk. The result of the data analysis is visualized into a web application system to enable the analysis results to be interpretable and readable by the user using Plotly visualization tool. Testing phases have shown that the classifier successfully classified tweets’ sentiments with 84.43% accuracy. Functionality testing is done to ensure that all the requirements are met. System usability testing is also done to ensure that the system flow is as intended and from the System Usability Scale (SUS), the system achieved an average final score of 87%. The future work that can be apply into this project is to include other languages other than English to identify people who are having suicidal ideation from different countries. 2023 Thesis https://ir.uitm.edu.my/id/eprint/88979/ https://ir.uitm.edu.my/id/eprint/88979/1/88979.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Human behavior Behaviorism Neobehaviorism Behavioral psychology
spellingShingle	Human behavior Behaviorism Neobehaviorism Behavioral psychology Alias, Annasuha Atie Atirah Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
description	People are more comfortable sharing their thoughts on social media rather than someone in person. Twitter is increasingly investigated as a means of detecting mental health status, including depression and suicidality, in the population. Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. It is essential to develop a machine learning system for early detection of suicidal ideation by analyzing his or her posts on social media. The prevailing methods of identifying and addressing suicidal content on social media exhibit limitations. Relying on human experts for identification is both costly and time-consuming. Additionally, the vast volume of data on platforms like Twitter makes manual outreach impractical. Existing machine learning models for sentiment analysis and suicide prediction lack a comprehensive user-friendliness system for the end users. Thus, this project aims to design, develop, and evaluate web-based application utilizing sentiment analysis, specifically employing the Naïve Bayes algorithm, to identify and analyze suicidal ideation within Twitter posts. By harnessing natural language processing techniques and data visualization tools, the project seeks to provide user-friendly solution for early detection and prevention of suicidal intentions expressed on social media. The goal is to empower suicide prevention organizations and concerned individuals with an efficient and accessible means of taking timely actions toward individuals at risk. The result of the data analysis is visualized into a web application system to enable the analysis results to be interpretable and readable by the user using Plotly visualization tool. Testing phases have shown that the classifier successfully classified tweets’ sentiments with 84.43% accuracy. Functionality testing is done to ensure that all the requirements are met. System usability testing is also done to ensure that the system flow is as intended and from the System Usability Scale (SUS), the system achieved an average final score of 87%. The future work that can be apply into this project is to include other languages other than English to identify people who are having suicidal ideation from different countries.
format	Thesis
qualification_level	Bachelor degree
author	Alias, Annasuha Atie Atirah
author_facet	Alias, Annasuha Atie Atirah
author_sort	Alias, Annasuha Atie Atirah
title	Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
title_short	Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
title_full	Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
title_fullStr	Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
title_full_unstemmed	Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
title_sort	identifying suicidal ideation through twitter sentiment analysis using naïve bayes / annasuha atie atirah alias
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/88979/1/88979.pdf
_version_	1794192182705389568

Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias

Similar Items