Detecting Irony on Greek Political Tweets
Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, Katia Lida Kermanidis · 2015
The present work describes the classification schema for irony detection in Greek political tweets. The proposed approach relies on limited labeled training data, and its performance on a larger unlabeled dataset is evaluated qualitatively (implicitly) via a correlation study between the irony that a party receives on Twitter, its respective actual election results during the Greek parliamentary elections of May 2012, and the difference between these results and the ones of the preceding elections of 2009. The machine learning results on the labeled dataset were highly encouraging and uncovered a trend whereby the volume of ironic tweets can predict the fluctuation from previous elections.