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Irony and sarcasm are two complex linguistic phenomena that are widely usedin everyday language and especially over the social media, but they representtwo serious issues for automated text understanding. Many labeled corpora havebeen extracted from several sources to accomplish this task, and it seems thatsarcasm is conveyed in different ways for different domains. Nonetheless, verylittle work has been done for comparing different methods among the availablecorpora. Furthermore, usually, each author collects and uses their own datasetsto evaluate his own method. In this paper, we show that sarcasm detection canbe tackled by applying classical machine learning algorithms to input textssub-symbolically represented in a Latent Semantic space. The main consequenceis that our studies establish both reference datasets and baselines for thesarcasm detection problem that could serve the scientific community to testnewly proposed methods.

Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection