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We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter

What it is about

Most ironic tweets contrast a literal positive evaluation with a situation everyone knows to be bad («working a double on New Year's Eve», «my car won't start»). On the authors' hand-annotated irony corpus, 671 such target situations were marked with their prototypical («implicit») sentiment — 665 negative and only 6 positive, confirming that irony mostly says something nice about something bad. Two ways of finding that sentiment automatically are compared: looking the words up in the SenticNet knowledge base and summing their polarities (weak, because the meaning of a phrase is not the sum of its words; whole multiword concepts do better), and searching Twitter for the target phrase and taking the majority sentiment of the tweets found (better, but coverage is limited to recent tweets). A polarity-contrast rule (explicit evaluation vs implicit target polarity) used alone is very precise but misses much, finds ironic tweets the SVM misses, and helps the SVM a little as a feature or in an AND/OR hybrid.

Ideas

For Mova

34. Irony and figurative language §4 (cues), §9–10 (computational irony; Mova's detector). Code: prag/src/irony.rs (situation lexicon, thresholds), prag/src/frames.rs (reaction frames for counting). Measures: irony-candidate precision and recall on the silver; size and hand-checked precision of the situation lexicon. SenticNet and ConceptNet-derived resources are not for release (licence), only as a comparison in tests.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00337

@article{hee2018we,
  title = {{We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter}},
  author = {Hee, Cynthia Van and Lefever, Els and Hoste, Véronique},
  journal = {Computational Linguistics},
  year = {2018},
  doi = {10.1162/coli\_a\_00337},
  url = {https://doi.org/10.1162/coli_a_00337}
}

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