Investigating the Importance of Hyperboles to Detect Sarcasm Using Machine Learning Techniques

Abstract

The present study aims to improve sarcasm detection mechanisms using multiple hyperboles such as interjection, intensifiers, capital letters, punctuation, and elongated words. A non-bias dataset consisting of the current pandemic related hashtags was used, namely #Chinesevirus and #Kungflu. Analysis and evaluation were performed with three distinguished machine learning algorithm that is Support Vector Machine, Random Forest and Random Forest with bagging classifiers. Each feature were analysed and the most significant hyperbole identifying sarcasm was assessed further by combining with other hyperboles. The experiments and analysis conducted using these hyperboles concluded that as a single or combined features, hyperboles enhance sarcasm especially in an unbiased dataset.