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Irony is an important device in human com-munication, both in everyday spoken con-versations as well as in written texts includ-ing books, websites, chats, reviews, and Twitter messages among others. Specific cases of irony and sarcasm have been stud-ied in different contexts but, to the best of our knowledge, only recently the first pub-licly available corpus including annotations about whether a text is ironic or not has been published by Filatova (2012). How-ever, no baseline for classification of ironic or sarcastic reviews has been provided. With this paper, we aim at closing this gap. We formulate the problem as a supervised classification task and evaluate different classifiers, reaching an F1-measure of up to 74 % using logistic regression. We analyze the impact of a number of features which have been proposed in previous research as well as combinations of them. 1
Buschmeier et al. (Wed,) studied this question.
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