Sarcasm Detection in News Headlines using Supervised Learning

Nowadays, social media has an enormous amount of news content with a sarcastic message. It is often expressed in the form of verbal and non-verbal. In this paper, the authors aim to identify sarcasm in news headlines using supervised learning. We address this task with the Bag-of-words features, context-independent features, and context-dependent features. Specifically, the authors employ seven supervised learning models, namely, Naïve Bayes-support vector machine, logistic regression, bidirectional gated recurrent units, Bidirectional encoders representation from Transformers (BERT), DistilBERT, and RoBERTa. Our experimental results indicate that RoBERTa achieves a better performance than others.