---
title: "Harnessing Cognitive Features for Sarcasm — Mishra 2016"
description: "The paper investigates whether the way people read can help machines recognize sarcasm in short, non-contextual texts such as reviews and tweets."
authors: ["Abhijit Mishra", "Diptesh Kanojia", "Seema Nagar", "Kuntal Dey", "Pushpak Bhattacharyya"]
year: 2016
venue: "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P16-1104/"
doi: "10.18653/v1/p16-1104"
arxiv: "1701.05574"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/mishra-2016-harnessing-cognitive-features-sarcasm-detection/"
---
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# Harnessing Cognitive Features for Sarcasm Detection

**Authors:** [Abhijit Mishra](https://md.research.elamur.ai/free/authors/abhijit-mishra/), [Diptesh Kanojia](https://md.research.elamur.ai/free/authors/diptesh-kanojia/), Seema Nagar, [Kuntal Dey](https://md.research.elamur.ai/free/authors/kuntal-dey/), [Pushpak Bhattacharyya](https://md.research.elamur.ai/free/authors/pushpak-bhattacharyya/) · **Year:** 2016 · **Venue:** Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P16-1104/) · [PDF](https://aclanthology.org/P16-1104.pdf) · [DOI 10.18653/v1/p16-1104](https://doi.org/10.18653/v1/p16-1104) · [arXiv 1701.05574](https://arxiv.org/abs/1701.05574) · [OpenAlex](https://openalex.org/W2512470422)

**Citations:** 63 citations (openalex-snapshot · 2026-10-14) · [22](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

The paper investigates whether the way people read can help machines recognize sarcasm in short, non-contextual texts such as reviews and tweets. Textual cues alone often fail here. The authors argue that sarcasm usually involves incongruity, which forces readers to re-process the text and leaves traces in their eye movements. They use their earlier freely released eye-tracking dataset of 1,000 short texts, read by seven participants with an SR-Research Eyelink-1000 tracker. They show that sarcastic texts receive markedly longer fixations per word. They then design two kinds of cognitive features: simple gaze measures (fixations, forward and backward saccades) and features derived from a graph whose nodes are words and whose edges are saccades. These are combined with the linguistic and stylistic features of the strongest prior detector (Joshi et al., 2015) and fed to several classifiers, including a multi-instance MILR model. The enriched feature set raises F-score by 3.7% over the best reported system. This suggests that cognitive signals, which could eventually come from cheap mobile eye-trackers, can complement text-based NLP.

## Key points

- **Dataset composition:** 350 sarcastic texts (103 from quote websites, 76 Amazon Movie Corpus reviews, 171 #sarcasm tweets) and 650 non-sarcastic texts, each 10–40 words long.
- **Avoiding the priming effect:** participants only labeled polarity and were never told to look for sarcasm. For all seven readers, the difference in average fixation duration between sarcastic and literal texts was highly significant (t-test).
- **Statistical significance:** a McNemar test comparing the MILR model with all features against an SVM using only the Joshi et al. (2015) features gave p = 0.02 and an odds ratio of 1.43.
- **Feature importance:** in rankings by Information Gain and Chi-squared tests, 16 of the top 20 features were gaze-based. The two highest-ranked features were the mean time readers fixated on each word and the position of the biggest backward eye movement.
- **Reading time alone is not enough:** substituting plain reading time for the gaze features produced no statistically significant gain. A key limitation is that the method needs eye-tracking data at test time.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

22 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 11 not yet.

- [Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection](https://md.research.elamur.ai/free/papers/ataei-2020-applying-transformers-aspect-based-sentiment/)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://md.research.elamur.ai/free/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/)
- [Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/papers/ghosh-2018-sarcasm-analysis-conversation-context/)
- [Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity](https://md.research.elamur.ai/free/papers/ghosh-2020-interpreting-verbal-irony-linguistic-strategies/)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://md.research.elamur.ai/free/papers/ghosh-2020-report-2020-sarcasm-detection-shared/)
- [SemEval-2018 Task 3: Irony Detection in English Tweets](https://md.research.elamur.ai/free/papers/hee-2018-semeval-2018-task-3-irony/)
- [Automatic Sarcasm Detection: A Survey](https://md.research.elamur.ai/free/papers/joshi-2016-automatic-sarcasm-detection-survey/)
- [Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network](https://md.research.elamur.ai/free/papers/mishra-2017-learning-cognitive-features-gaze-data/)
- [A Modular Architecture for Unsupervised Sarcasm Generation](https://md.research.elamur.ai/free/papers/mishra-2019-modular-architecture-unsupervised-sarcasm-generation/)
- [Reasoning with Sarcasm by Reading In-Between](https://md.research.elamur.ai/free/papers/tay-2018-reasoning-sarcasm-reading-between/)
- [Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data](https://md.research.elamur.ai/free/papers/wang-2020-building-bridge-method-image-text/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mishra2016harnessing,
  title = {{Harnessing Cognitive Features for Sarcasm Detection}},
  author = {Mishra, Abhijit and Kanojia, Diptesh and Nagar, Seema and Dey, Kuntal and Bhattacharyya, Pushpak},
  booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2016},
  doi = {10.18653/v1/p16-1104},
  eprint = {1701.05574},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p16-1104}
}
```

- [← previous Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://md.research.elamur.ai/free/papers/selvaraju-2016-grad-cam-visual-explanations-deep/)
- [next → Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends'](https://md.research.elamur.ai/free/papers/joshi-2016-harnessing-sequence-labeling-sarcasm-detection/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/papers/2016/page/2/)

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