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This research aims to develop a robust framework for detecting sarcasm in text by leveraging conversational context and advanced classification techniques.

April 25, 2026Multimedia Tools and ApplicationsOpen Access

Multi-sarcasm and consarcasm: a contextual dataset and hybrid model for fine-grained sarcasm detection

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This research aims to develop a robust framework for detecting sarcasm in text by leveraging conversational context and advanced classification techniques.
Developed ConSarcasm framework using hierarchical context modeling and hybrid Transformer-CNN strategy.
Introduced Multi-Sarcasm dataset with three levels of sarcasm intensity, validated through zero-shot learning and human review.
Employed a custom Byte Pair Encoding tokenizer for handling unique lexical cues like emojis.
Achieved an accuracy of 89.99% and an F1-score of 90.02% in sarcasm detection on Multi-Sarcasm dataset.
Generalized well to binary sarcasm detection, with accuracies of 80.29% on IAC V2 and 93.70% on Binary-Sarcasm dataset.

Authors

DMDalila MekhzoumiUniversity of Béjaïa SMSiham MehidiUniversity of Béjaïa KAKamal AmrounUniversity of Béjaïa

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Implication

Randomized trial shows improved sarcasm detection in social media texts, indicating advanced NLP capabilities.

Cite This Study

Mekhzoumi et al. (2026) studied this question.

synapsesocial.com/papers/69ec598788ba6daa22dab63e https://doi.org/10.1007/s11042-026-21511-3

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Multi-sarcasm and consarcasm: a contextual dataset and hybrid model for fine-grained sarcasm detection

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Cite This Study

Multi-sarcasm and consarcasm: a contextual dataset and hybrid model for fine-grained sarcasm detection

Key Points

Discussion

Implication

Cite This Study

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