Exploring Author Context for Detecting Intended vs Perceived Sarcasm
Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold.
['Model', 'Model SIARN (baseline)', 'Riloff 0.711', 'Ptacek 0.863']
User embeddings show remarkable predictive power on the Ptacek dataset. In particular, using the EX-W-CASCADE model, we get better results (f1-score 0.922) than the baseline (f1-score 0.863) without even looking at the tweet being predicted. On the Riloff dataset, however, user embeddings seem to be far less informative, with EX-W-CASCADE yielding an f1-score of only 0.478. Out of the exclusive models, we get the highest f1-score of 0.546 using EX-ED on Riloff. By contrast we get 0.873 on Ptacek using EX-ED.