---
title: "Understanding the Sarcastic Nature of Emojis with SarcOji (2022)"
description: "Detecting sarcasm is difficult because it often depends on context that a model cannot see. 29 references."
authors: ["Vandita Grover", "Hema Banati"]
year: 2022
venue: "Proceedings of the Fifth International Workshop on Emoji Understanding and Applications in Social Media"
source: "https://aclanthology.org/2022.emoji-1.4/"
doi: "10.18653/v1/2022.emoji-1.4"
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/en/papers/grover-2022-understanding-sarcastic-nature-emojis-sarcoji/"
---
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# Understanding the Sarcastic Nature of Emojis with SarcOji

**Authors:** [Vandita Grover](https://md.research.elamur.ai/free/en/authors/vandita-grover/) and Hema Banati · **Year:** 2022 · **Venue:** [Proceedings of the Fifth International Workshop on Emoji Understanding and Applications in Social Media](https://md.research.elamur.ai/free/en/venues/emoji/) ([International Workshop on Emoji Understanding and Applications in Social Media 2022](https://md.research.elamur.ai/free/en/venues/emoji/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.emoji-1.4/) · [PDF](https://aclanthology.org/2022.emoji-1.4.pdf) · [DOI 10.18653/v1/2022.emoji-1.4](https://doi.org/10.18653/v1/2022.emoji-1.4) · [OpenAlex](https://openalex.org/W4287854818)

**Citations:** 5 citations (OpenAlex, 2026-10-14)

## Summary
Detecting sarcasm is difficult because it often depends on context that a model cannot see. Emojis may carry useful cues, but few sarcasm corpora contain them. The authors address this by building SarcOji, an English dataset in which every record contains at least one emoji. They merged the train and test portions of five public sarcasm collections drawn from Twitter, Facebook and Reddit. They then unified the labels, removed non-English items, stripped URLs, mentions and hashtags, and kept only texts with emojis.

Each record is enriched with several derived features:
- text and emoji sentiment scores from SentiWordNet, VADER and TextBlob, plus Emoji Sentiment Ranking for the dominant emoji;
- the most frequent emoji in the text (MaxEmoji);
- how often MaxEmoji repeats;
- where it first appears.

The analysis found little polarity clash between sarcastic text and its emojis. Instead, emoji repetition and emoji placement separated the two classes more clearly. The dataset is released publicly as a starting point for emoji-aware sarcasm detection.

## Key points
- SarcOji has 29,377 labeled records: 11,448 sarcastic and 17,929 non-sarcastic. These records were pooled from five earlier resources: iSarcasmEval (SemEval-2022), the FigLang 2020 shared task, Oprea and Magdy (2019), the corpus of Subramanian et al. (2019), and the one by Ghosh and Veale (2016).
- Repeated use of the same emoji in one text ("intensity") was about twice as common in the sarcastic subset. Face with tears of joy is the MaxEmoji in roughly 19% of texts in both classes, but it is repeated more often when the text is sarcastic.
- MaxEmoji position differs between the classes:

  | Class | Beginning | Middle | End |
  |---|---|---|---|
  | Non-sarcastic | 6.9% | 12.76% | 80.34% |
  | Sarcastic | 13.75% | 18.88% | 67.36% |

  The non-sarcastic pattern matches earlier work by Kralj Novak et al. and Zhao et al.; the sarcastic pattern does not.
- The four sentiment tools disagreed on the top-5 emojis. SWN, VADER and TextBlob gave a score of 0 to some emojis, which weakens any polarity-incongruence test.
- Contrary to expectation, sarcastic texts and their emojis tended to be more positive than non-sarcastic ones, and no strong text–emoji polarity mismatch appeared.

## Interesting ideas
- Record each non-verbal marker together with its repetition count and its relative position (start, middle or end), rather than just its presence; the paper shows these two features separate sarcasm better than polarity does.
- A marker placed mid-sentence, next to the content it comments on, behaves differently from one appended at the end as a closing tag; end-placement looked like plain annotation, while internal placement was more typical of sarcasm.
- A planned "polarity clash" signal can fail to appear in real data; here sarcasm co-occurred with uniformly positive surface sentiment, so incongruence alone is too narrow a test.
- Running several sentiment lexicons side by side exposed large disagreement and silent zero scores for symbols, which is a cheap way to audit lexical resources before trusting them.
- Building a task corpus by harmonizing several heterogeneous public datasets, with unified labels and a filter keeping only items with the target phenomenon, yields a focused evaluation set at low cost.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): The tokenizer could attach a "repeat count" and a "relative position" attribute to emoji, emoticon and punctuation-run tokens, so later stages can use them without re-scanning raw text.
- **I2** (frames): The irony detector in prag could treat an emoji attached to a specific clause or argument in the UD tree as an explainable cue that scopes over that clause.
- **I3** (frames): Irony rules should not rely only on positive-word-versus-negative-situation contrast; excessive positivity relative to the described world state could be an additional cue.
- **I4** (learn): When compiling value or polarity tables from multiple witnesses, flag items where sources disagree or default to zero, and route them to an LLM teacher tier instead of averaging.
- **I5** (learn): A small irony or sarcasm test set could be assembled the same way, with emoji-free sarcastic items as a negative control for emoji-based rules.

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

## Institutions

- [University of Delhi](https://md.research.elamur.ai/free/en/universities/university-of-delhi/) — University, New Delhi, India · 5 works · 15 citations

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## Cite

Cite as: Vandita Grover and Hema Banati. 2022. Understanding the Sarcastic Nature of Emojis with SarcOji. Proceedings of the Fifth International Workshop on Emoji Understanding and Applications in Social Media.

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

```bibtex
@inproceedings{grover2022understanding,
  title = {{Understanding the Sarcastic Nature of Emojis with SarcOji}},
  author = {Vandita Grover and Hema Banati},
  booktitle = {Proceedings of the Fifth International Workshop on Emoji Understanding and Applications in Social Media},
  year = {2022},
  doi = {10.18653/v1/2022.emoji-1.4},
  url = {https://doi.org/10.18653/v1/2022.emoji-1.4}
}
```

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## References

29 references, 13 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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*Updated 1 November 2026*