Commonsense-Aware Sarcasm Detection with Heterogeneous Graph Attention Network

Sarcasm is a sophisticated expression, commonly used on social media, e.g., Reddit and Twitter. The presence of sarcasm in social media text flips the polarity of sentiment, thus hindering the performance of works that require true sentiment, e.g., sentiment analysis and opinion mining. However, current works fail to exploit commonsense knowledge in the sarcasm detection task. In this paper, we revisit sarcasm detection from a novel perspective, which models commonsense knowledge as well as context semantics to reason with sarcasm. More specifically, we propose a commonsense-aware model with a heterogeneous graph attention network that leverages commonsense knowledge, enabling it to better understand implied sentiment behind the literal meaning. We conduct experiments on benchmark datasets from Reddit and Internet Argument Corpus. Experimental results show that our proposed approach yields superior performance with commonsense knowledge integrated.