Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection

Sawhney, R; Shah, RR; Bhatia, V; Lin, CT; Aggarwal, S; Prasad, M

Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection

Sawhney, R Shah, RR Bhatia, V Lin, CT

Aggarwal, S Prasad, M

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2019, 2019-June
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Sawhney, R	en_US
dc.contributor.author	Shah, RR	en_US
dc.contributor.author	Bhatia, V	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Aggarwal, S	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2019, 2019-June	en_US
dc.identifier.isbn	9781538617281	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137358
dc.description.abstract	© 2019 IEEE. The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2019.8858989	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-June	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-June	en_US

Abstract:

© 2019 IEEE. The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm.

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http://hdl.handle.net/10453/137358