"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition
This work addresses the challenge of irony detection for natural language processing applications in social media, but it is incremental as it builds on existing marker-based approaches.
The study tackled the problem of recognizing irony in social media by analyzing theoretically grounded irony markers on Twitter and Reddit, finding that typographic markers like emoticons are most discriminative for Twitter, while morphological markers like interjections are most discriminative for Reddit.
Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media platforms: $Twitter$ and $Reddit$. Classification and frequency analysis show that for $Twitter$, typographic markers such as emoticons and emojis are the most discriminative markers to recognize ironic utterances, while for $Reddit$ the morphological markers (e.g., interjections, tag questions) are the most discriminative.