Sarcasm Detection an Explainable AI Approach for Reddit Political Text
Rupali Amit Bagate, Aparna Shashikant Joshi, Anup Kadam, Chandan Kumar Choubey, Nilesh Pandurang Sable, Ajay Kumar, Namrata Dogra, Durgesh Nandan · Mathematical Modelling and Engineering Problems · 2025
Sentiment analysis, often known as opinion mining, determines how people feel about any subject.Sarcasm is the expression of irony or mocking through the use of derogatory language.These phrases change the polarity of a positive feeling into a bad one or the other way around.The focus of the proposed research is to implement a domain-oriented sarcasm detection model with a XAI approach to justify classification results.This strategy emphasizes on bringing value to domain-specific text using an explainable approach.The research referred to political domain content from Reddit platform to get insights in the area.It identifies sarcastic context from textual information present on social media.As a part of sentiment analysis and sarcasm detection natural language processing (NLP) plays important role.The suggested methodology is identifying sarcasm using weighted average approach of long short term memory (LSTM) and support vector classifier (SVC).implemented system has a fresh strategy to identify such sarcastic terms from sentences or forecast such sarcastic sentences.The suggested strategy used an Explainable Artificial Intelligence (XAI) approach to identify sarcasm in a specific political domain text.As a part of XAI, counterfactual explanation is implemented to identify the sarcastic words from given text which is given with more weightage by model while training the model.The system has generated 75.75% F1 score as result of weighted average approach.