# Systematic Literature Review: Computational Approaches for Humour Style Classification

arXiv: https://arxiv.org/abs/2402.01759 · HTML: https://mlsanity.com/paper/2402.01759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 113 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [iSarcasm: A Dataset of Intended Sarcasm](https://mlsanity.com/paper/1911.03123/) (2019)
- [Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations](https://mlsanity.com/paper/2105.09984/) (2021)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- ["President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines](https://mlsanity.com/paper/1906.00274/) (2019)
- [The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress](https://mlsanity.com/paper/2207.05691/) (2022)
- [ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor](https://mlsanity.com/paper/2004.12765/) (2020)
- [We Are Humor Beings: Understanding and Predicting Visual Humor](https://mlsanity.com/paper/1512.04407/) (2015)
- [A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection](https://mlsanity.com/paper/1805.11869/) (2018)
- [Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition](https://mlsanity.com/paper/2012.12007/) (2020)
- [Hybrid Multimodal Fusion for Humor Detection](https://mlsanity.com/paper/2209.11949/) (2022)
- [M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations](https://mlsanity.com/paper/2108.01260/) (2021)
- [Multimodal Analysis of memes for sentiment extraction](https://mlsanity.com/paper/2112.11850/) (2021)
- [The Naughtyformer: A Transformer Understands Offensive Humor](https://mlsanity.com/paper/2211.14369/) (2022)

## Built on by (most cited first, 6 of 6)

- [Who's Laughing Now? An Overview of Computational Humour Generation and Explanation](https://mlsanity.com/paper/2509.21175/) (2025)
- [Trick or Neat: Adversarial Ambiguity and Language Model Evaluation](https://mlsanity.com/paper/2506.01205/) (2025)
- [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)
- [Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis](https://mlsanity.com/paper/2501.02891/) (2025)
- [Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion](https://mlsanity.com/paper/2608.26879/) (2026)
- [MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions](https://mlsanity.com/paper/2609.11322/) (2026)

## Abstract (arXiv)

Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition. In this systematic literature review (SLR), we survey the landscape of computational techniques applied to these related tasks and also uncover their fundamental relevance to humour style analysis. Through this study, we unveil common approaches, illuminate various datasets and evaluation metrics, and effectively navigate the complex terrain of humour research. Our efforts determine potential research gaps and outlined promising directions. Furthermore, the SLR identifies a range of features and computational models that can seamlessly transition from related tasks like binary humour and sarcasm detection to invigorate humour style classification. These features encompass incongruity, sentiment and polarity analysis, ambiguity detection, acoustic nuances, visual cues, contextual insights, and more. The computational models that emerge contain traditional machine learning paradigms, neural network architectures, transformer-based models, and specialised models attuned to the nuances of humour. Finally, the SLR provides access to existing datasets related to humour and sarcasm, facilitating the work of future researchers.