A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities.

Sarcasm prevalent in social media poses challenges for sentiment analysis applications by flipping polarity, thus increasing the demand for sarcasm detection. In this article, we present a systematic survey of the research on sarcasm detection. We discuss the definition, problem formulation, datasets, as well as comprehensively review and evaluate methods that can detect sarcasm from three perspectives: the incongruity it contains, the sentimental cues it conveys, and the commonsense knowledge it implies. Specially, we detail sarcasm-related fundamental theories across disciplines, which may enhance sarcasm detection by leveraging interdisciplinary research and hope to facilitate collaborative efforts across research fields. We also discuss a variety of open problems, along with future opportunities for sarcasm detection. [ABSTRACT FROM AUTHOR]