Implementation of emotional features on satire detection

Abstract

Recognition of satirical language in social multimedia outlets turns out to be a trending research area in computational linguistics. Many researchers have analyzed satirical language from the various point of views: lexically, syntactically, and semantically. However, due to the ironic dimension of emotion embedded in the language, emotional study of satirical language has ever left behind. This paper proposes the emotion-based detection system for satirical figurative language processing. These emotional features are extracted using emotion lexicon: EmoLex and sentiment lexicon: VADER. Ensemble bagging technique is used to tackle the problem of ambiguous nature of emotion. Experiments are carried out on both short text and long text configurations namely news articles, Amazon product reviews, and tweets. Recognition of satirical language can aid in lessening the impact of implicit language in public opinion mining, sentiment analysis, fake news detection and cyberbullying.