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Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of nonverbal cues such as emojis in social media, in this work we study the role of these structures in irony detection. Since the existing irony detection datasets have <10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.

Dataset Analysis and Augmentation for Emoji-Sensitive Irony Detection