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