  The use of irony and sarcasm in social media allows us to study them at scale
for the first time. However, their diversity has made it difficult to construct
a high-quality corpus of sarcasm in dialogue. Here, we describe the process of
creating a large- scale, highly-diverse corpus of online debate forums
dialogue, and our novel methods for operationalizing classes of sarcasm in the
form of rhetorical questions and hyperbole. We show that we can use
lexico-syntactic cues to reliably retrieve sarcastic utterances with high
accuracy. To demonstrate the properties and quality of our corpus, we conduct
supervised learning experiments with simple features, and show that we achieve
both higher precision and F than previous work on sarcasm in debate forums
dialogue. We apply a weakly-supervised linguistic pattern learner and
qualitatively analyze the linguistic differences in each class.
