Representing Social Media Users For Sarcasm Detection

Y. Alex Kolchinski, Christopher Potts . Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018 – 41 citations

[Paper]    
Datasets EMNLP Image Text Integration Interdisciplinary Approaches

We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors’ propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.

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