Multimodal affect analysis of psychodynamic play therapy

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dc.authoridSalah, Albert Ali/0000-0001-6342-428X|Doyran, Metehan/0000-0002-9016-955X
dc.authorwosidSalah, Albert Ali/E-5820-2013
dc.authorwosidDoyran, Metehan/AAG-5411-2020
dc.contributor.authorHalfon, Sibel
dc.contributor.authorDoyran, Metehan
dc.contributor.authorTurkmen, Batikan
dc.contributor.authorOktay, Eda Aydin
dc.contributor.authorSalah, Ali Albert
dc.date.accessioned2024-07-18T20:45:12Z
dc.date.available2024-07-18T20:45:12Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractObjective: We explore state of the art machine learning based tools for automatic facial and linguistic affect analysis to allow easier, faster, and more precise quantification and annotation of children's verbal and non-verbal affective expressions in psychodynamic child psychotherapy. Method: The sample included 53 Turkish children: 41 with internalizing, externalizing and comorbid problems; 12 in the non-clinical range. We collected audio and video recordings of 148 sessions, which were manually transcribed. Independent raters coded children's expressions of pleasure, anger, sadness and anxiety using the Children's Play Therapy Instrument (CPTI). Automatic facial and linguistic affect analysis modalities were adapted, developed, and combined in a system that predicts affect. Statistical regression methods (linear and polynomial regression) and machine learning techniques (deep learning, support vector regression and extreme learning machine) were used for predicting CPTI affect dimensions. Results: Experimental results show significant associations between automated affect predictions and CPTI affect dimensions with small to medium effect sizes. Fusion of facial and linguistic features work best for pleasure predictions; however, for other affect predictions linguistic analyses outperform facial analyses. External validity analyses partially support anger and pleasure predictions. Discussion: The system enables retrieving affective expressions of children, but needs improvement for precision.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [215K180]en_US
dc.description.sponsorshipThis study was partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) Project No: 215K180.en_US
dc.identifier.doi10.1080/10503307.2020.1839141
dc.identifier.endpage417en_US
dc.identifier.issn1050-3307
dc.identifier.issn1468-4381
dc.identifier.issue3en_US
dc.identifier.pmid33148118en_US
dc.identifier.scopus2-s2.0-85095771546en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage402en_US
dc.identifier.urihttps://doi.org/10.1080/10503307.2020.1839141
dc.identifier.urihttps://hdl.handle.net/11411/7445
dc.identifier.volume31en_US
dc.identifier.wosWOS:000585482500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherRoutledge Journals, Taylor & Francis Ltden_US
dc.relation.ispartofPsychotherapy Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMultimodal Affect Analysisen_US
dc.subjectFace Analysisen_US
dc.subjectText Analysisen_US
dc.subjectPsychodynamic Play Therapyen_US
dc.subjectExtreme Learning-Machineen_US
dc.subjectFacial Expressionsen_US
dc.subjectEmotionen_US
dc.subjectPsychotherapyen_US
dc.subjectCoregulationen_US
dc.subjectChildrenen_US
dc.subjectBehavioren_US
dc.subjectAssociationsen_US
dc.subjectRecognitionen_US
dc.subjectRegressionen_US
dc.titleMultimodal affect analysis of psychodynamic play therapyen_US
dc.typeArticleen_US

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