Representing Social Media Users for Sarcasm Detection
This work addresses sarcasm detection for social media analysis, presenting an incremental improvement by integrating user-specific representations into existing models.
The authors tackled sarcasm detection in social media by exploring two user representation methods: a Bayesian approach for author sarcasm propensities and dense embeddings for author-text interactions, showing that augmenting a bidirectional RNN with these improved performance, with the Bayesian method effective in homogeneous contexts and embeddings beneficial in diverse ones.
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.