# A Transformer-based approach to Irony and Sarcasm detection

arXiv: https://arxiv.org/abs/1911.10401 · HTML: https://mlsanity.com/paper/1911.10401/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

## Built on by (most cited first, 28 of 28)

- [A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis](https://mlsanity.com/paper/2306.03650/) (2023)
- [On Sarcasm Detection with OpenAI GPT-based Models](https://mlsanity.com/paper/2312.04642/) (2023)
- [Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection](https://mlsanity.com/paper/2310.01430/) (2023)
- [Sarcasm Detection: A Comparative Study](https://mlsanity.com/paper/2107.02276/) (2021)
- [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://mlsanity.com/paper/2209.04712/) (2022)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024)
- [Irony Detection, Reasoning and Understanding in Zero-shot Learning](https://mlsanity.com/paper/2501.16884/) (2025)
- [Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching](https://mlsanity.com/paper/2403.11984/) (2024)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection](https://mlsanity.com/paper/2312.03706/) (2023)
- [A Robust Deep Ensemble Classifier for Figurative Language Detection](https://mlsanity.com/paper/2107.04372/) (2021)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [Sarcasm Detection Framework Using Context, Emotion and Sentiment Features](https://mlsanity.com/paper/2211.13014/) (2022)
- [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://mlsanity.com/paper/2211.16883/) (2022)
- [Evaluating Large Language Models' Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios](https://mlsanity.com/paper/2309.10744/) (2023)
- [Gen-AI for User Safety: A Survey](https://mlsanity.com/paper/2411.06606/) (2024)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [CEA-LIST at CheckThat! 2025: Evaluating LLMs as Detectors of Bias and Opinion in Text](https://mlsanity.com/paper/2507.07539/) (2025)
- [RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection](https://mlsanity.com/paper/2601.17002/) (2026)
- [Zero-Resource Multi-Dialectal Arabic Natural Language Understanding](https://mlsanity.com/paper/2104.06591/) (2021)
- [Researchers eye-view of sarcasm detection in social media textual content](https://mlsanity.com/paper/2304.08582/) (2023)
- [Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features](https://mlsanity.com/paper/2311.04885/) (2023)
- [Was that Sarcasm?: A Literature Survey on Sarcasm Detection](https://mlsanity.com/paper/2412.00425/) (2024)
- [Assessing how hyperparameters impact Large Language Models' sarcasm detection performance](https://mlsanity.com/paper/2504.06166/) (2025)
- [A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection](https://mlsanity.com/paper/2604.08381/) (2026)

## Abstract (arXiv)

Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of Natural Language Processing (NLP), mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper we employ advanced Deep Learning (DL) methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work [71], we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which, is further enhanced with the employment and devise of a recurrent convolutional neural network (RCNN). With this set-up, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state of the art methodologies and systems. Results demonstrate that the proposed methodology achieves state of the art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.