Representing Social Media Users for Sarcasm Detection
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2018
- DOI:
- 10.48550/arXiv.1808.08470
- arXiv:
- arXiv:1808.08470
- Bibcode:
- 2018arXiv180808470K
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Social and Information Networks
- E-Print:
- To appear in EMNLP 2018