Modelling Context with User Embeddings for Sarcasm Detection in Social Media
This addresses the problem of improving sarcasm detection for social media analysis by automating context modeling, though it is incremental over prior work that used manual feature engineering.
The paper tackles sarcasm detection in social media by proposing a deep neural network that learns user embeddings from previous posts to capture contextual speaker information, outperforming a state-of-the-art feature-engineered approach.
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 our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.