# Sarcasm Detection Framework Using Context, Emotion and Sentiment Features

arXiv: https://arxiv.org/abs/2211.13014 · HTML: https://mlsanity.com/paper/2211.13014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [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)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [Indonesian Social Media Sentiment Analysis With Sarcasm Detection](https://mlsanity.com/paper/1505.03085/) (2015)

## Built on by (most cited first, 2 of 2)

- [A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection](https://mlsanity.com/paper/2503.11838/) (2025)
- [An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework](https://mlsanity.com/paper/2411.01264/) (2024)

## Abstract (arXiv)

Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.