Satire Detection from Web Documents Using Machine Learning Methods

Satire exposes humanity´s vices and foibles through the use of irony, wit, and sometimes sarcasm too. It is also frequently used in online communities. Recognition of satire can help in many NLP applications like dialogue system and review summarization. In this paper we filter online news articles as satirical or true news documents using SVM (Support Vector Machine) classification method combined with machine learning techniques. With ample training documents SVM tends to give good classification results. For obtaining promising results with SVM an understanding of its working and ways to influence its accuracy is required. We also use various feature extraction strategies and conclude that TF-IDF-BNS feature extraction gives maximum accuracy for detection of satire in web content.