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A common form of sarcasm on Twitter con-sists of a positive sentiment contrasted with a negative situation. For example, many sarcas-tic 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 iden-tify this type of sarcasm in tweets. We present a novel bootstrapping algorithm that automati-cally learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets. We show that identifying contrast-ing contexts using the phrases learned through bootstrapping yields improved recall for sar-casm recognition. 1
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Riloff et al. (Tue,) studied this question.
synapsesocial.com/papers/69d98e43a1d151c65f6847af — DOI: https://doi.org/10.18653/v1/d13-1066
Ellen Riloff
University of Arizona
Ashequl Qadir
Philips (Finland)
Prafulla Surve
University of Utah
University of Utah
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