Linguistic Features in Automatic Sarcasm Detection

What it is about

On the Twitter part of the FigLang 2020 sarcasm shared task (2,500 sarcastic and 2,500 non-sarcastic training tweets, each with its preceding tweets as context; labels from hashtags), the authors test interpretable features with a Random Forest: counts (characters, punctuation), complexity and style, and psychological features (LIWC categories, valence–arousal–dominance norms, VADER sentiment, a six-emotion classifier), plus the similarity of the response to its context in word embeddings and in emotion. Sarcastic replies shift emotion away from the context (an angry context answered with «joy»), sincere replies stay emotionally close. The response alone reaches F1 0.67 with all features; adding the two previous tweets raises it to 0.70, roughly the shared task's LSTM baseline.

Ideas

• I1. Add an emotional-shift check to the irony candidates in prag/src/irony.rs: a positive reply (joy, praise, thanks) right after a narrated or quoted negative event or an angry line from another character is a context incongruity, the dialogue form of the implicit contrast already in the rule. Polarity of the previous frame comes from Mova's own polarity lexicon and the polarity field of the previous sentence. Measure precision on silver lines whose means names irony.
• I2. Nothing to take from the feature set itself: LIWC and the VAD norms are licensed resources and the gains are small; Mova's own cue lists and frames carry the same information transparently.

For Mova

34. Irony and figurative language §4 (cues of irony), §10. Code: prag/src/irony.rs, prag/src/feats.rs (previous sentence polarity). Measure: irony-candidate precision on silver with and without the shift check.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.4000/books.aaccademia.8485

@inproceedings{ducret2020linguistic,
  title = {{Linguistic Features in Automatic Sarcasm Detection}},
  author = {Ducret, Martina and Kruse, Lauren and Martinez, Carlos and Feldman, Anna and Peng, Jing},
  booktitle = {Proceedings of the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020)},
  year = {2020},
  doi = {10.4000/books.aaccademia.8485},
  url = {https://doi.org/10.4000/books.aaccademia.8485}
}

References

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