SCX-SD: Semi-supervised Method for Contextual Sarcasm Detection

Sarcasm detection is to identify the text with the author’s sarcastic attitude. Verbal sarcasm is one main error sources of sentiment analysis tasks. However, labeled sarcastic samples are expensive to obtain. Previous approaches, e.g., model user and topic embedding from multiple perspectives together with large-scale network training, are not suitable for real business scenarios that expect low cost and high speed. In this paper, we propose a semi-supervised method for contextual sarcasm detection in online discussion forums. We adopt author and topic sarcastic prior preference as context embedding that supply simple but representative background knowledge. Then we introduce a sarcasm-unlabeled learning method to utilize a few labeled sarcastic samples and model the classification boundary. Experiments are conducted on real-world data from Reddit, and the results indicate the outperformance over existing methods.