SINCERE: A Hybrid Framework With Graph-Based Compact Textual Models Using Emotion Classification and Sentiment Analysis for Twitter Sarcasm Detection

Abstract

Sarcasm is an expression of contempt expressed through verbal irony. It is a nuanced form of language that individuals use to imply the opposite of what they are actually saying, and thus it can be difficult to detect at times. The lack of large, annotated datasets is one of the major challenges and limitations of building systems to detect sarcasm automatically. To address this issue, we propose a hybrid graph-based framework, namely, SINCERE, to build compact sarcasm detection models with sentiment and emotion analysis by leveraging only a small amount of prior data. To automatically extract patterns from a small dataset collected by distant supervision, a graph is first constructed. This approach is used to discover latent representations of vertices in a network, as the basis for a language model. We demonstrate that simple classifiers built from the model can detect sarcasm and generalize better than the state-of-the-art approach. According to the experimental results, the proposed SINCERE framework is able to outperform the SOTA baselines on accuracy by 5%.