A Comparative Review of Deep Learning Techniques on the Classification of Irony and Sarcasm in Text

This paper provides a review of classification methods for irony and sarcasm in textual data. It explores different approaches to detecting irony and sarcasm, their definitions, distinguishing features, and detection methodologies. The study examines a range of datasets used in irony and sarcasm detection research, including short-text datasets from social media platforms and long-text datasets from product reviews and discussion forums. Additionally, the paper discusses the various features employed in sarcasm detection experiments, such as lexical, pragmatic, hyperbole, semantic, syntactic, sentiment, and contextual features. It also explores the classification methodologies used. The paper concludes by analyzing each classification method and highlighting the latest trends in irony and sarcasm detection.