Sarcasm detection on news headlines using transformers

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dc.authorid0000-0002-9502-7817
dc.authorid0000-0002-9619-8247
dc.contributor.authorGümüşçekiçci, Gizemen_US
dc.contributor.authorDehkharghani, Rahimen_US
dc.date.accessioned2025-09-23T06:54:43Z
dc.date.available2025-09-23T06:54:43Z
dc.date.issued2025-09-07
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.descriptionThanks to the Graduate school of Isik University for their support.en_US
dc.description.abstractSarcasm poses a linguistic challenge due to its figurative nature, where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text, as a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting to the readers. In the proposed methodology, we experimented with Transformers, namely the BERT model, and several Machine and Deep Learning models with different word and sentence embedding methods. The proposed approach inherently requires high-performance resources due to the use of large-scale pre-trained language models such as BERT. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86% F1-score when applied to the extended news headlines dataset, which we made publicly available on GitHub.en_US
dc.description.sponsorshipGraduate school of Isik Universityen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationGümüşçekiçci, G. & Dehkharghani, R. (2025). Sarcasm detection on news headlines using transformers. Journal of Supercomputing, 81(14), 1-28. doi:en_US
dc.identifier.doi10.1007/s11227-025-07773-y
dc.identifier.endpage28
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue14
dc.identifier.scopus2-s2.0-105015147847
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6717
dc.identifier.urihttp://dx.doi.org/10.1007/s11227-025-07773-y
dc.identifier.volume81
dc.identifier.wosWOS:001565025900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorGümüşçekiçci, Gizemen_US
dc.institutionauthorid0000-0002-9502-7817
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Supercomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectHandcrafted featuresen_US
dc.subjectNews headlinesen_US
dc.subjectSarcasmen_US
dc.subjectSarcasm classificationen_US
dc.subjectText augmentationen_US
dc.subjectTransformersen_US
dc.subjectLearning systemsen_US
dc.subjectLinguisticsen_US
dc.subjectSentiment analysisen_US
dc.subjectHuman communicationsen_US
dc.subjectLiteralsen_US
dc.subjectSocial mediaen_US
dc.titleSarcasm detection on news headlines using transformersen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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