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CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/C18-1156.pdf

Summary

Sarcasm detection has mostly relied on surface signals in the text, such as interjections, punctuation or sentiment contrasts. Much sarcasm in online forums carries no such markers and makes sense only with knowledge of the speaker and the surrounding conversation. The authors present CASCADE, a hybrid classifier for Reddit comments that combines three representations. The first is a CNN encoding of the comment itself. The second is a user embedding built from each author's posting history. The third is a discourse vector describing the subreddit where the comment appears. For the user embedding, ParagraphVector captures writing style, a personality-oriented CNN captures character traits, and canonical correlation analysis (CCA) merges the two views. The discourse vector comes from document-level modeling of all comments in a forum. On a large Reddit corpus, tested in balanced, imbalanced and politics-only (Pol) settings, CASCADE beats strong baselines, including the earlier best system, CUE-CNN. The work shows that modeling both the user and the thread is key to recognizing sarcasm that depends on context.

Key points

  • With the content CNN alone, accuracy is roughly 10 points below the full model. Adding user embeddings brings the biggest jump, about 8–12 points in accuracy and F1.
  • The full CASCADE model scores Acc/F1 of 0.78/0.77 on Main balanced, 0.79/0.86 on Main imbalanced and 0.74/0.75 on Pol.
  • Discourse features matter most on the Pol subset, where they add about 3 points of F1.
  • Replacing CCA with plain concatenation of the user features clearly lowers performance. Dropping the personality features brings results down to roughly CUE-CNN's level.
  • A t-SNE plot of the user embeddings shows sarcastic users spread more widely than non-sarcastic ones (variance 10.92 vs 5.20). Errors cluster around users with few past posts and around very long context comments.

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: Discourse analysis

Cited by

51 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 46 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. arXiv 1805.06413

@inproceedings{hazarika2018cascade,
  title = {{CASCADE: Contextual Sarcasm Detection in Online Discussion Forums}},
  author = {Hazarika, Devamanyu and Poria, Soujanya and Gorantla, Sruthi and Cambria, Erik and Zimmermann, Roger and Mihalcea, Rada},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  eprint = {1805.06413},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1805.06413}
}

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