Modelling Context with User Embeddings for Sarcasm Detection in Social Media

Silvio Amir1, Byron C. Wallace2, Hao Lyu3, Paula Carvalho1, Mario J. Silva1
1INESC-ID, IST, 2University of Texas at Austin, 3iSchool, University of Texas at Austin


We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that the our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.