Implementation of emotional features on satire detection

Recognition of satirical language in social multimedia outlets turn out to be a trending research area in computational linguistics. Many researchers have analyzed satirical language from various point of views: lexically, syntactically, and semantically. However, due to the ironic dimension of emotion embedded in satirical language, emotional study of satirical language has ever left behind. In this study, we propose the new emotion-based satire detection model using supervised and unsupervised weighting approaches (TFRF and TFIDF). We implement the model with Ensemble Bagging classifier compared with benchmark classifier: SVM. The model not only outperform the word-based baseline: BoW but also handle both short text and long text configurations. Our work in recognition of satirical language can aid in lessening the impact of implicit language in public opinion mining, sentiment analysis, fake news detection and cyberbullying.