# Harnessing Cognitive Features for Sarcasm Detection

arXiv: https://arxiv.org/abs/1701.05574 · HTML: https://mlsanity.com/paper/1701.05574/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [Sarcasm Detection: A Comparative Study](https://mlsanity.com/paper/2107.02276/) (2021)
- [Cognition-aware Cognate Detection](https://mlsanity.com/paper/2112.08087/) (2021)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models](https://mlsanity.com/paper/2212.09873/) (2022)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [Researchers eye-view of sarcasm detection in social media textual content](https://mlsanity.com/paper/2304.08582/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized. Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds. This presence of incongruity- implicit or explicit- affects the way readers eyes move through the text. We observe the difference in the behaviour of the eye, while reading sarcastic and non sarcastic sentences. Motivated by his observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features obtained from readers eye movement data. We perform statistical classification using the enhanced feature set so obtained. The augmented cognitive features improve sarcasm detection by 3.7% (in terms of F-score), over the performance of the best reported system.