AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: MARIA-MĂDĂLINA MIRCEA.
DOI: 10.24193/subbi.2020.2.02
Published Online: 2020-10-27
Published Print: 2020-12-30
pp. 17-30
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Abstract. Research focused on mental health-related issues is vital to the modern person’s life. Specific phobias are part of the anxiety disorder umbrella and they are distressing afflictions. Emetophobia is the rarely known, yet fairly common and highly disruptive specific phobia of vomiting. Unlike other phobias, emetophobia is triggered not only by the object of the specific fear, but also by verbal and written mentions of said object. This paper proposes and compares ten neural network-based architectures that discern between triggering and non-triggering groups of written words. An interface is created, where the best models can be used in emetophobia-related applications. This interface is then integrated into an application that can be used by emetophobes to censor online content such that the exposure to triggers is controlled, patient-centered, and patient-paced.
Received by the editors: 24 August 2020.
2010 Mathematics Subject Classiffication. 68T05, 68T50.
1998 CR Categories and Descriptors. I.2.6 [Artificial Intelligence]: Learning - Induction; I.2.7 [Artificial Intelligence]: Natural Language Processing - Text analysis.
Key words and phrases. machine learning, text classiffication, long short-term memory, trigger detection, natural language processing, neural networks.
