Sarcasm detection on news headlines using transformers

Sarcasm poses a linguistic challenge due to its figurative nature where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text is a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting for the readers. In the proposed methodology, we experimented with Transformers, namely the Bert model, and several Machine and Deep Learning models with different word and sentence embedding methods. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86\% F1-score when applied to our extended news headlines dataset, which is publicly available on Github.