# Sarcasm Detection: A Comparative Study

arXiv: https://arxiv.org/abs/2107.02276 · HTML: https://mlsanity.com/paper/2107.02276/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://mlsanity.com/paper/1610.00883/) (2016)
- [Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue](https://mlsanity.com/paper/1708.08572/) (2017)
- [Harnessing Cognitive Features for Sarcasm Detection](https://mlsanity.com/paper/1701.05574/) (2017)
- [Sarcasm Detection using Context Separators in Online Discourse](https://mlsanity.com/paper/2006.00850/) (2020)

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

- [On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojis](https://mlsanity.com/paper/2504.05603/) (2025)
- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches](https://mlsanity.com/paper/2211.08029/) (2022)
- [Evaluating Large Language Models' Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios](https://mlsanity.com/paper/2309.10744/) (2023)
- [Token-free Models for Sarcasm Detection](https://mlsanity.com/paper/2505.01006/) (2025)

## Abstract (arXiv)

Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective computing systems performing sentiment analysis. This article compiles and reviews the salient work in the literature of automatic sarcasm detection. Thus far, three main paradigm shifts have occurred in the way researchers have approached this task: 1) semi-supervised pattern extraction to identify implicit sentiment, 2) use of hashtag-based supervision, and 3) incorporation of context beyond target text. In this article, we provide a comprehensive review of the datasets, approaches, trends, and issues in sarcasm and irony detection.