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# How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature

**Authors:** [Aditya Joshi](https://md.mova.elamur.ai/free/en/docs/papers/authors/aditya-joshi/), Vaibhav Tripathi, [Pushpak Bhattacharyya](https://md.mova.elamur.ai/free/en/docs/papers/authors/pushpak-bhattacharyya/), Mark Carman, Meghna Singh, Jaya Saraswati, Rajita Shukla · **Year:** 2016 · **Venue:** ALTA 2016 **Link:** [aclanthology.org/U16-1013](https://aclanthology.org/U16-1013/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the quotes come from a book-reviews website with user tags — not reusable

[**4** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/joshi-2016-how-challenging-is-sarcasm-versus/) of the 140673 papers whose bibliographies we read[Proceedings of the Australasian Language Technology Association Workshop 2016](https://aclanthology.org/volumes/U16-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/U16-1013/)
- [PDF](https://aclanthology.org/U16-1013.pdf)

**Authors (7)**

1. Aditya Joshi
2. Vaibhav Tripathi
3. Pushpak Bhattacharyya
4. Mark Carman
5. Meghna Singh
6. Jaya Saraswati
7. Rajita Shukla

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper asks whether sarcasm (irony with a target of ridicule, so it changes sentiment towards that target) can be told from plain irony, using 4,306 quotes from literature with user tags (753 sarcasm, 677 irony, 2,876 philosophy). Three experienced annotators labelled 501 of them: their agreement with the tags is lower for sarcasm vs irony than for sarcasm vs philosophy (for one annotator kappa 0.62 vs 0.65, the same trend for all three); confusions come from self-deprecation and from contempt expressed without positive words. An SVM with the incongruity features of Joshi et al. (2015) reaches average F 0.846 for sarcasm vs philosophy (0.80 class-balanced) but only 0.654 for sarcasm vs irony. The authors note that a sarcastic quote can become a sincere one by changing a single word, and vice versa.

## Ideas

- I1. Literary quotes, not tweets, are the closest outside material to Mova's tales, and the result says the sarcasm/irony border is hard even for people. Keep Mova's irony output at one level (ironic or not) plus the target, and decide sarcasm only by the explicit target rule (a person, especially the addressee, as the object of the reversed evaluation; see `zeng-2022-survey-automatic-irony-processing-linguistic` I2) rather than by a learned classifier.
- I2. The one-word switch observation is a ready minimal-pair test for `prag/src/irony.rs`: pairs that differ in one evaluative or situational word («a woman needs a man like a fish needs a bicycle» vs «… like a fish needs water») must flip the candidate decision. Write a dozen such pairs by hand from public-domain sayings and keep them as a gate with negative controls.

## For Mova

[34. Irony and figurative language](https://md.mova.elamur.ai/free/en/docs/languages/en/34-irony-and-figurative-language/) §2 (verbal irony and sarcasm), §10 (Mova's detector), §12 (figurative language in the tales). Code: `prag/src/irony.rs`. Measure: the minimal-pair gate (all pairs must flip).

## Cite

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

```
@inproceedings{joshi2016how,
  title = {{How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature}},
  author = {Joshi, Aditya and Tripathi, Vaibhav and Bhattacharyya, Pushpak and Carman, Mark and Singh, Meghna and Saraswati, Jaya and Shukla, Rajita},
  booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2016},
  year = {2016},
  url = {https://aclanthology.org/U16-1013/}
}
```

- [← previous Learning a POS tagger for AAVE-like language](https://md.mova.elamur.ai/free/en/docs/papers/jorgensen-2016-learning-pos-tagger-aave-like/)
- [next → Detecting Sarcasm Using Different Forms Of Incongruity](https://md.mova.elamur.ai/free/en/docs/papers/joshi-2017-detecting-sarcasm-different-forms-incongruity/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

22 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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5. David K Elson and Kathleen McKeown. 2010. Automatic attribution of quoted speech in literary narrative. In AAAI. Citeseer. [find](https://scholar.google.com/scholar?q=David+K+Elson+and+Kathleen+McKeown%2E+2010%2E+Automatic+attribution+of+quoted+speech+in+literary+narrative%2E+In+AAAI%2E+Citeseer%2E)
6. Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers - Volume 2, HLT ’11, pages 581–586, Stroudsburg, PA, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Roberto+Gonza%CC%81lez%2DIba%CC%81n%CC%83ez%2C+Smaranda+Muresan%2C+and+Nina+Wacholder%2E+2011%2E+Identifying+sarcasm+in+twitter%3A+A+closer+look%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Li)
7. Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015. Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, volume 2, pages 757–762. [find](https://scholar.google.com/scholar?q=Aditya+Joshi%2C+Vinita+Sharma%2C+and+Pushpak+Bhattacharyya%2E+2015%2E+Harnessing+context+incongruity+for+sarcasm+detection%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Lingu)
8. Aditya Joshi, Kevin Patel, Vaibhav Tripathi, Pushpak Bhattacharyya, and Mark J Carman. 2016. Are word embedding-based features useful for sarcasm detection? In EMNLP. [find](https://scholar.google.com/scholar?q=Aditya+Joshi%2C+Kevin+Patel%2C+Vaibhav+Tripathi%2C+Pushpak+Bhattacharyya%2C+and+Mark+J+Carman%2E+2016%2E+Are+word+embedding%2Dbased+features+useful+for+sarcasm+detection%3F+In+EMNLP%2E)
9. Roger J. Kreuz and Gina M. Caucci. 2007. Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language, FigLanguages ’07, pages 1– 4, Stroudsburg, PA, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Roger+J%2E+Kreuz+and+Gina+M%2E+Caucci%2E+2007%2E+Lexical+influences+on+the+perception+of+sarcasm%2E+In+Proceedings+of+the+Workshop+on+Computational+Approaches+to+Figurative+Language%2C+FigLanguages+%E2%80%9907%2C+pages+1%E2%80%93+)
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12. Stephanie Lukin and Marilyn Walker. 2013. Really? well. apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue. [find](https://scholar.google.com/scholar?q=Stephanie+Lukin+and+Marilyn+Walker%2E+2013%2E+Really%3F+well%2E+apparently+bootstrapping+improves+the+performance+of+sarcasm+and+nastiness+classifiers+for+online+dialogue%2E)
13. Diana Maynard and Mark A Greenwood. 2014. Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. [find](https://scholar.google.com/scholar?q=Diana+Maynard+and+Mark+A+Greenwood%2E+2014%2E+Who+cares+about+sarcastic+tweets%3F+investigating+the+impact+of+sarcasm+on+sentiment+analysis%2E)
14. Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815. [↗](https://arxiv.org/abs/1610.08815)
15. Antonio Reyes, Paolo Rosso, and Davide Buscaldi. 2012. From humor recognition to irony detection: The figurative language of social media. Data Knowl. Eng., 74:1–12, April. [find](https://scholar.google.com/scholar?q=Antonio+Reyes%2C+Paolo+Rosso%2C+and+Davide+Buscaldi%2E+2012%2E+From+humor+recognition+to+irony+detection%3A+The+figurative+language+of+social+media%2E+Data+Knowl%2E+Eng%2E%2C+74%3A1%E2%80%9312%2C+April%2E)
16. Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalin-dra De Silva, Nathan Gilbert, and Ruihong Huang. 2013a. Sarcasm as contrast between a positive sentiment and negative situation. [find](https://scholar.google.com/scholar?q=Ellen+Riloff%2C+Ashequl+Qadir%2C+Prafulla+Surve%2C+Lalin%2Ddra+De+Silva%2C+Nathan+Gilbert%2C+and+Ruihong+Huang%2E+2013a%2E+Sarcasm+as+contrast+between+a+positive+sentiment+and+negative+situation%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
17. Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalin-dra De Silva, Nathan Gilbert, and Ruihong Huang. 2013b. Sarcasm as contrast between a positive sentiment and negative situation. In EMNLP, volume 13, pages 704–714. [find](https://scholar.google.com/scholar?q=Ellen+Riloff%2C+Ashequl+Qadir%2C+Prafulla+Surve%2C+Lalin%2Ddra+De+Silva%2C+Nathan+Gilbert%2C+and+Ruihong+Huang%2E+2013b%2E+Sarcasm+as+contrast+between+a+positive+sentiment+and+negative+situation%2E+In+EMNLP%2C+volume+13%2C) [our card](https://md.mova.elamur.ai/free/en/docs/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
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20. Yuchun Tang, Yan-Qing Zhang, Nitesh V Chawla, and Sven Krasser. 2009. Svms modeling for highly imbalanced classification. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39(1):281–288. [find](https://scholar.google.com/scholar?q=Yuchun+Tang%2C+Yan%2DQing+Zhang%2C+Nitesh+V+Chawla%2C+and+Sven+Krasser%2E+2009%2E+Svms+modeling+for+highly+imbalanced+classification%2E+Systems%2C+Man%2C+and+Cybernetics%2C+Part+B%3A+Cybernetics%2C+IEEE+Transactions+on%2C+39%281)
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