Sarcasm Detection on Text for Political Domain— An Explainable Approach

In the era of social media, a large volume data is generated by applications such as industrial internet things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence big tools plays an important role in devising mechanisms for handling this vast per required usage to form information from unstructured data. When publicly available on it imperative treat carefully respect sentiments individuals. paper, authors have attempted solve three problems treating using AI science tools, weighted statistical methods, explainability sarcastic comments. The first objective research study sarcasm detection, next apply domain-specific political Reddit dataset. Moreover, last predict words counterfactual explainability. textare extracted self-annotated corpus dataset containing 533 million comments written English language, where 1.3 are sarcastic. detection based model uses average approach deep learning models extract provide output terms content classification. Identifying sentence very challenging when has that flips polarity positive sentiment into negative sentiment. This cumbersome task can be achieved with artificial intelligenceand machine learningalgorithms train assist classifying sentences keep media posts acceptable society. There should mechanism determine extent which model's prediction could relied upon. Therefore, explination essential. We studied methods developed detecting explaining prediction. assists identifying sarcasmfrom reddit post its score classify given textcorrectly. F1-score 75.75% 80% proves robustness proposed model.