Irony detection on microposts with limited set of features

Abstract

Detecting irony in texts attracts computer scientists' attention as a recent research problem. Automatic detection of irony on microblog texts, i.e., microposts, poses additional challenges. Microposts have limited number of characters, and generally include typing errors, therefore traditional methods of text mining cannot be applied easily. This study aims to automatically detect irony in microposts. The proposed solution is based on supervised learning through a limited set of features extracted from the text. Experimental results show the effectiveness of the approach for Turkish and English informal texts.