Abstract
We investigate the impact of using author context on textual sarcasm detection. We define
author context as the embedded representation of their historical posts on Twitter and
suggest neural models that extract these representations. We experiment with two tweet
datasets, one labelled manually for sarcasm,
and the other via tag-based distant supervision.
We achieve state-of-the-art performance on the
second dataset, but not on the one labelled
manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.