Sarcasm detection algorithm: Unraveling ironic expressions with precision

Abstract

In sarcasm, the public expresses their unfavourable feelings by utilising positive words in the text. Humans find it extremely difficult to recognise. By doing so, we can do fascination with sarcasm detection in tweets and other social media text. In this research, we investigate a novel pattern-based methodology for sarcasm recognition and a behavioural modelling strategy for efficient sarcasm detection through the analysis of tweet content while simultaneously utilising user activity features gained from previous behaviours. The accuracy and efficacy are evaluated using a variety of classifiers, including Random Forest, Support Vector Machine (SVM), k Nearest Neighbors (kNN), and Maximum Entropy.