A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as "love" or "enjoy", followed by an expression that describes an undesirable activity or state (e.g., "taking exams" or "being ignored"). We have developed a sarcasm recognizer to identify this type of sarcasm in tweets. We present a novel bootstrapping algorithm that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets. We show that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition.
Finally REF built a model to detect sarcasm with a bootstrapping algorithm that automatically learn lists of positive sentiments phrases and negative situation phrases from sarcastic tweet, in order to detect the characteristic of sarcasm of being a contrast between positive sentiment and negative situation.
Sarcasm as Contrast between a Positive Sentiment and Negative Situation
EMNLP
Computer Science