Modelling Context with User Embeddings for Sarcasm Detection in Social Media

TL;DR

This paper presents a deep neural network that automatically learns user embeddings from social media posts to improve sarcasm detection, outperforming previous feature-engineering-based methods.

Contribution

It introduces a novel approach to automatically learn user embeddings for sarcasm detection, reducing reliance on manual feature engineering.

Findings

Outperforms state-of-the-art sarcasm detection models

Uses only previous posts to learn user embeddings

Enhances detection accuracy by incorporating user context

Abstract

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.