A machine learning approach in analysing the effect of hyperboles using negative sentiment tweets for sarcasm detection

This paper investigates negative sentiment tweets with the presence of hyperboles for sarcasm detection. Six thousand and six hundred pre-processed negative sentiment tweets comprising #Chinesevirus, #Kungflu, #COVID19, #Hantavirus and #Coronavirus were gathered for sarcasm detection. Five hyperbole features, namely interjection, intensifier, capital letter, punctuation mark and elongated word were analysed using three renowned machine learning algorithms, that is, Support Vector Machine, Random Forest, and Random Forest with Bagging. With the presence of hyperbolic words in the tweets in an unbiased dataset, the proposed model with elongated word achieved an accuracy and F-score of 78.74% and 71%, respectively. Intensifier was found to be the most significant hyperbole (p < .0001). Experiments and analysis conducted in this study concluded that hyperboles exist in an unbiased dataset which helps enhance the sarcasm detection as well.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper investigates negative sentiment tweets with hyperboles for sarcasm detection. The proposed model achieved high accuracy and F-score in detecting sarcasm in tweets that contain hyperbolic words.