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Predicting Suicide Ideation from Social Media Text Using CNN-BiLSTM · Peerford

conference paper · Publication · 2024

Predicting Suicide Ideation from Social Media Text Using CNN-BiLSTM

Christianah T. Oyewale, Joseph D. Akinyemi, Ayodeji O. J. Ibitoye, Olufade F. W. Onifade

Communications in computer and information science

Closed access
International · 3 countries

Go to the publisher 10.1007/978-3-031-53731-8_22

Citations: 4from other works
Field-weighted impact: 2.861.00 is the world average for its field and year
Among its field: 90th percentileby citations, against works of its field and year
Authors: 4from 4 institutions

Authors

4 authors across 4 institutions in 3 countries

Christianah T. OyewaleFirst authorCorresponding
- Université Mohammed VI Polytechnique (MA)
Joseph D. Akinyemi
Ayodeji O. J. Ibitoye
as printed: Ayodeji O.J Ibitoye
Olufade F. W. OnifadeSenior author
- University of Ibadan (NG)
as printed: Olufade F.W Onifade

Development goals

3Good health and well-being

Countries on it

United Kingdom

Also on it

University of Greenwicheducation · GB
University of Yorkeducation · GB

Fields

A work can sit in several, each with a score

Mental Health via WritingTopic
Suicide and Self-Harm StudiesTopic
Computational and Text Analysis MethodsTopic