Construction of Hybrid Model for English News Headline Sarcasm Detection by Word Embedding Technique

Abstract

People often use sarcasm to taunt, anger, or amuse one another. Scathing undertones can't be missed, even when using a simple sentiment analysis tool. Sarcasm may be detected using a variety of machine learning techniques, including rule-based approaches, statistical approaches, and classifiers. Since English is a widely used language on the internet, most of these terms were created to help people recognize sarcasm in written material. Convolutional Neural Networks (CNNs) are used to extract features, and Naive Bayes (NBs) are trained and evaluated on those features using a probability function. This suggested approach gives a more accurate forecast of sarcasm detection based on probability prediction. This hybrid machine learning technique is evaluated according to the stretching component in frequency inverse domain, the cluster of the words and word vectors with embedding. Based on the findings, the proposed model surpasses many advanced algorithms for sarcasm detection, including accuracy, recall, and F1 scores. It is possible to identify sarcasm in a multi-domain dataset using the suggested model, which is accurate and resilient.