Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets

What it is about

3,000 English tweets collected by #irony, #sarcasm and #not were all labelled by hand with a fine-grained scheme instead of trusting the hashtags: ironic by a polarity clash, other verbal irony, situational irony, or not ironic (2,396 ironic, 604 not; kappa 0.72), with harsh (mocking) irony flagged, and below tweet level the evaluations, their modifiers, their targets and the target's implicit polarity from world knowledge («the most hideous spider, that makes me feel sooo much better»). An SVM with lexical, syntactic, sentiment-lexicon (six lexicons, with a polarity-contrast feature and negation flipping) and Word2Vec-cluster features reaches F1 67.7 when the irony hashtags are removed, and 92.8 when they are kept — barely above the 87% accuracy of «hashtag present = ironic». Lexical features alone do almost as well as all combined; the authors conclude that modelling the clash between explicit evaluations and implicit target polarity is the way forward.

Ideas

• I1. Make the explanation of an irony candidate in prag/src/irony.rs follow the paper's annotation units: the evaluation span, its modifier (intensifier), its target, and the target's implicit polarity with its source (polarity lexicon, a negative situation from the seed list, or a narrated frame). The incongruent pair is already printed; adding target and source makes the decision checkable by a reader.
• I2. Measure Mova's irony detection with and without the explicit narrator markers that play the role of #not in tales (said slyly, mocked, sneered, jeered, teased, ironically): add them as a narrator-frame cue in the Say frame, and report results both with the cue and with it masked. The masked figure is the honest one, as the paper's hashtag experiment shows (92.8 vs 67.7).
• I3. Add situational irony as a separate label the reader can recognise from frames: a plan or want frame whose outcome frame is its opposite (the trap catches the trapper). This is the «irony of fate» class of the paper's scheme and is common in fables; a rule over cause/next links with negated or reversed roles, checked on Aesop fables against the teacher's means.

For Mova

34. Irony and figurative language §2 (verbal irony and sarcasm; types), §4 (cues), §10 (Mova's irony detector), §12 (figurative language in the tales). Code: prag/src/irony.rs, prag/src/frames.rs (narrator cue, situational irony). Measures: irony-candidate precision/recall on silver with narrator cues masked and unmasked.

Cite

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

@inproceedings{hee2016monday,
  title = {{Monday mornings are my fave :) \#not Exploring the Automatic Recognition of Irony in English tweets}},
  author = {Hee, Cynthia Van and Lefever, Els and Hoste, Véronique},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  year = {2016},
  url = {https://aclanthology.org/C16-1257/}
}

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