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Automatic Sarcasm Detection: A Survey

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/1602.03426

Summary

Sarcasm pairs positive-sounding wording with a negative intended meaning, so it often misleads sentiment analysis systems. That has made detecting it automatically a research problem of its own. The authors describe this survey as the first compilation of computational work on the topic. It traces the field from Tepperman et al. (2006), who used speech cues, through text-based studies up to 2016. It covers linguistic theories of sarcasm and the different ways the task has been framed: binary classification, multi-class setups with irony and humor labels, sequence labeling over dialogue turns and word sense disambiguation. It also groups datasets into short text, long text and other sources, and reviews rule-based, semi-supervised and supervised methods. The main resource is a comparison table that places about forty papers by data form, annotation strategy, approach and feature type. The authors name three turning points in the field: semi-supervised mining of patterns that carry implicit sentiment, labels derived from hashtags, and context taken from outside the target text. For sentiment work, the survey gives a map of datasets, recurring problems and open directions.

Key points

  • Tweets are the most common data source. Labels come either from manual annotation (e.g., Riloff et al. 2013) or from distant supervision using hashtags such as #sarcasm. Reddit, reviews, forum posts and TV-series dialogue are also used.
  • Most work is on English, with smaller efforts in Chinese, Italian, Czech, Dutch, Greek, Indonesian and Hindi.
  • Class imbalance is a recurring problem. For example, only 12.5% of tweets in Barbieri et al.'s Italian dataset are sarcastic, while Rakov and Rosenberg built a balanced 15k-tweet set. Proposed fixes include:
    • ensemble learning with majority voting
    • SVM-perf optimised for F-score
    • L1/L2 regularisation that treats contextual features and bag-of-words features differently
    • reporting AUC instead of F-score
  • Many systems still use surface sentiment as a feature. Bouazizi and Ohtsuki report about 4% higher accuracy when sentiment classification knows whether a text is sarcastic.
  • Suggested future directions are implicit sentiment detection, sentiment carried by numbers, under-studied forms of sarcasm (like-prefixed and illocutionary), culture-specific annotation differences, and deep learning architectures.

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

Topics: Word sense disambiguation

Cited by

113 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 100 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1602.03426 · arXiv 1602.03426

@misc{joshi2016automatic,
  title = {{Automatic Sarcasm Detection: A Survey}},
  author = {Joshi, Aditya and Bhattacharyya, Pushpak and Carman, Mark James},
  year = {2016},
  doi = {10.48550/arxiv.1602.03426},
  eprint = {1602.03426},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1602.03426}
}

References

54 references, 11 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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