An emoji centric approach to sarcasm detection in online discourse.

ABSTRACT: Sarcasm detection has gained significance in sentiment analysis, especially when social media is rife with cyberbullying and trolling. Emojis have garnered researchers' interest as they are polysemic. But they are usually employed in combination with other modalities in sarcasm detection. This work proposes an emoji-focused approach to study their role in sarcasm detection. This structured approach studies how emojis, sentiment of text and emojis, most frequent emoji in text, and its position helps with sarcasm classification task. Experiments include various machine learning classifiers and BERT fine-tuned for emojis to reveal the decisive role of emojis in sarcasm detection, even in the absence of any other modality. Novel sarcasm-aware GloVe-based emoji embeddings are presented that out

SUBMITTER: Grover V 

PROVIDER: S-EPMC12855789 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature