With 1 follower I must be AWESOME :P.Exploring the role of irony markers in irony recognition
Table 2: Ablation Tests of irony markers for Twitter. bold are best scores (in %).
['Features', 'Category', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
We first conduct a binary classification task to decide whether an utterance (e.g., a tweet or a Reddit post) is ironic or non-ironic, exclusively based on the irony marker features. We use Support Vector Machines (SVM) classifier with linear kernel We report Precision (P), Recall (R) and F1 scores of both I and NI categories. This is primarily due to the removal of hyperboles that frequently appear in ironic utterances in Twitter. Removing typographic markers (e.g., emojis, emoticons, etc.)