---
title: "#SarcasmDetection is soooo general! Towards a"
description: "Parde and Nielsen build a feature set meant to work across domains. 24 references. Among them: 0."
authors: ["Natalie Parde", "Rodney D. Nielsen"]
year: 2018
venue: "arXiv:1806.03369"
source: "https://arxiv.org/abs/1806.03369"
doi: "10.48550/arxiv.1806.03369"
arxiv: "1806.03369"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/parde-2018-sarcasmdetection-soooo-general-towards-domain/"
---
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# #SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm

**Authors:** [Natalie Parde](https://md.research.elamur.ai/free/en/authors/natalie-parde/) and [Rodney D. Nielsen](https://md.research.elamur.ai/free/en/authors/rodney-nielsen/) · **Year:** 2018 · **Venue:** arXiv:1806.03369 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1806.03369) · [PDF](https://arxiv.org/pdf/1806.03369) · [DOI 10.48550/arxiv.1806.03369](https://doi.org/10.48550/arxiv.1806.03369) · [arXiv 1806.03369](https://arxiv.org/abs/1806.03369) · [OpenAlex](https://openalex.org/W2729417315)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Sarcasm detectors are usually tuned to a single source, most often Twitter, and they tend to fail when moved to text with different conventions. Parde and Nielsen build a feature set meant to work across domains. It is centred on sentiment polarity, subjectivity and PMI cues drawn from general lexicons. They compare it with Twitter-specific and Amazon-specific features, using a newly collected and publicly released Twitter corpus and Filatova's Amazon review sarcasm corpus. They test several training regimes: in-domain only, out-of-domain only, simple pooling, and pooling plus the EasyAdapt domain adaptation step. On their own, Twitter data is almost useless for classifying Amazon reviews. After EasyAdapt, however, the same tweets combined with a small Amazon set reach F1 0.780 on Amazon. This beats the baselines and cuts error by 14% relative to the previous best result on that corpus. The work matters because it shows that plentiful but mismatched data can help a target domain, provided the features generalize and the adaptation is explicit.

## Key points
- Twitter data was collected in February and March 2016:
  - tweets with a trailing #sarcasm were labeled sarcastic (1959);
  - tweets with a trailing Ekman-emotion hashtag (#happiness, #anger, etc.) were labeled non-sarcastic (3039);
  - the Amazon corpus has 437 sarcastic and 817 non-sarcastic reviews;
  - both datasets were split 80/20 into training and test sets.
- Retweets, @replies and tweets with links were removed. Only tweets with exactly one target hashtag at the end were kept, and that hashtag was stripped before training.
- The lexical resources were Liu05, MPQA, AFINN and Google Web1T. Five Weka classifiers were compared with 5-fold cross-validation, and Naïve Bayes performed best.
- Results on Amazon test data:
  - Twitter-only training gave F1 0.276;
  - combined training with EasyAdapt gave F1 0.780, compared with baselines of 0.515 and 0.345;
  - error fell from 0.256 to 0.220 compared with the prior best (Buschmeier et al., F1 0.744).
- Results on Twitter: in-domain F1 was 0.583, comparable to earlier reports of 0.51 and 0.545.
- Among the general features, polarity and subjectivity features worked well and PMI features were weaker. The authors suspect hashtag word-joining and unusual acronyms hurt PMI.

## Interesting ideas
- Pooling a large off-domain set with a small target set only paid off once an explicit domain adaptation step (EasyAdapt) was added; naive merging or source-only training failed, so the regime matters as much as data volume.
- The non-sarcastic class was built from emotionally charged texts, so the model cannot win by simply detecting strong sentiment; this is a built-in hard negative control.
- Labels were harvested only when the marker hashtag trailed the text, because a hashtag inside the body usually means the writer is talking about sarcasm rather than being sarcastic (mention versus use).
- Instead of learning tweet-specific positive-phrase/negative-situation pairs, the contrast between positive sentiment and a negative situation was approximated with general lexicons, keeping the core irony cue portable across domains.
- The error analysis found that whole reviews were labeled sarcastic although only one sentence was, and the most polar word often sat outside that sentence, which misleads document-level features.

## For Mova

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

- **I1** (learn): when silver data from mismatched genres (news, tweets, fables) is mixed, mark each example's genre so training can separate shared from genre-specific patterns rather than letting abundant genres swamp the target.
- **I2** (learn): irony and evaluation tests should include sincere emotional sentences as negatives, so we can check that the irony detector is not just a sentiment detector in disguise.
- **I3** (frames): the speech-act layer could separate mentioning a stance word ("this is sarcasm") from performing it, which also helps when cheaply harvesting silver labels.
- **I4** (frames): an irony rule could compare the polarity of evaluative words with the state the world model assigns to the situation (e.g., "great" plus a broken item), giving an explainable irony signal.
- **I5** (answer): irony judgments should be tied to the specific clause, so that answers about a text's attitude point to the exact sarcastic span rather than the whole document.

*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 North Texas](https://md.research.elamur.ai/free/en/universities/university-of-north-texas/) — University, Denton, United States · 18 works · 4,513 citations

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🧭 Topics (1): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Natalie Parde and Rodney D. Nielsen. 2018. #SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm. arXiv:1806.03369.

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

```bibtex
@misc{parde2018sarcasmdetection,
  title = {{\#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm}},
  author = {Natalie Parde and Rodney D. Nielsen},
  year = {2018},
  doi = {10.48550/arxiv.1806.03369},
  eprint = {1806.03369},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1806.03369}
}
```

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## References

24 references, 15 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 24 October 2026*