Multi-task learning on mental disorder detection, sentiment analysis, and emotion detection using social media posts

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dc.authorid0000-0001-5765-5735
dc.authorid0000-0002-9619-8247
dc.contributor.authorArmah, Courageen_US
dc.contributor.authorDehkharghani, Rahimen_US
dc.date.accessioned2025-08-18T06:29:05Z
dc.date.available2025-08-18T06:29:05Z
dc.date.issued2024
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractMental disorders such as suicidal behavior, bipolar disorder, depressive disorders, and anxiety have been diagnosed among the youth recently. Social media platforms such as Reddit have become popular for anonymous posts. People are far more likely to share on these social media platforms what they really feel like in their real lives when they are anonymous. It is thus helpful to extract people's sentiments and feelings from these platforms in training models for mental disorder detection. This study uses multi-task learning techniques to examine the estimation of behaviors and mental states for early mental disease diagnosis. We propose a multi-task system trained on three related tasks: mental disorder detection as the primary task, emotion analysis, and sentiment analysis as auxiliary tasks. We took the SWMH dataset, which included four main different mental disorders already labeled (bipolar, depression, anxiety, and suicide) and offmychest. We then added labels for emotion and sentiment to the dataset. The observed results are comparable to previous studies in the field and demonstrate that deep learning multi-task frameworks can improve the accuracy of related text classification tasks when compared to training them separately as single-task systems.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationArmah, C. & Dehkharghani, R. (2024). Multi-task learning on mental disorder detection, sentiment analysis, and emotion detection using social media posts. Paper presented at the 8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedings. doi:10.1109/ISAS64331.2024.10845733en_US
dc.identifier.doi10.1109/ISAS64331.2024.10845733
dc.identifier.isbn9798331540104
dc.identifier.scopus2-s2.0-85218059948
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11729/6618
dc.identifier.urihttps://doi.org/10.1109/ISAS64331.2024.10845733
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArmah, Courageen_US
dc.institutionauthorid0000-0001-5765-5735
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmotion detectionen_US
dc.subjectMental disorder detectionen_US
dc.subjectMulti-task learningen_US
dc.subjectNatural language processingen_US
dc.subjectSentiment analysisen_US
dc.subjectContrastive Learningen_US
dc.subjectEmotion recognitionen_US
dc.subjectAnalysis detectionen_US
dc.subjectLanguage processingen_US
dc.subjectMental disordersen_US
dc.subjectMultitask learningen_US
dc.subjectNatural languagesen_US
dc.subjectSocial media platformsen_US
dc.titleMulti-task learning on mental disorder detection, sentiment analysis, and emotion detection using social media postsen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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