An Improved Method for Detection of Satire from User-Generated Content

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. It is a sophisticated form of speech act widely used in online communities. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic in nature or not. Recognition of sarcasm may anticipate benefits in many sentiment analysis of NLP applications, such as safe search, review summary reports, engaging dialogue systems and review ranking applications and systems. Classification of online news articles for satire has been very much done in the manual way. In our system, we have experimented with an automated approach to classify online news article using the SVM (Support Vector Machine) classification method. SVM has been shown to give good classification results when ample training documents are given. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. Keywords-Sarcasm, Satire, Irony, Sentiment Analysis, Supervised and Unsupervised Classification, Irony Detection, Algorithm, Machine Learning, Polarity, Opinion Mining, Data Corpus, Toenization,Support Vector Machine.