  The literature in automated sarcasm detection has mainly focused on lexical,
syntactic and semantic-level analysis of text. However, a sarcastic sentence
can be expressed with contextual presumptions, background and commonsense
knowledge. In this paper, we propose CASCADE (a ContextuAl SarCasm DEtector)
that adopts a hybrid approach of both content and context-driven modeling for
sarcasm detection in online social media discussions. For the latter, CASCADE
aims at extracting contextual information from the discourse of a discussion
thread. Also, since the sarcastic nature and form of expression can vary from
person to person, CASCADE utilizes user embeddings that encode stylometric and
personality features of the users. When used along with content-based feature
extractors such as Convolutional Neural Networks (CNNs), we see a significant
boost in the classification performance on a large Reddit corpus.
