Emotion based voted classifier for Arabic irony tweet identification

Abstract

In this paper, we have worked on irony detection in the Arabic language, a task which is organized by FIRE 2019. The tweets have been preprocessed and tokenized to extract the frequency-based, emotion-based features. These features are used to irony identification using the voted classifier. The F-score of our proposed approach is 0.807 and the top-ranking developed method having F-score of .037, so the difference between F-score makes our approach better. © Copyright 2019 for this paper by its authors.