Exploring Author Context for Detecting Intended vs Perceived Sarcasm
Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold.
['Model', 'Riloff', '#Riloff']
To investigate further, we re-labelled the Riloff dataset via distant supervision considering these tags as markers of sarcasm, to create the #Riloff dataset. We applied the exclusive models on #Riloff and noticed a considerably higher predictive power than on Riloff. Author history seems therefore predictive of authorial sarcastic intention, but not of external perception. This could indicate that future work should differentiate between the two types of sarcasm: intended and perceived. Both are important to detect, for applications such as opinion mining for the former and hate speech detection for the latter.