An Effective Approach for Detection of Sarcasm in Tweets

Abstract

A Sarcasm detection system is important for applications like sentiment analyzer, review processing system and natural language processing systems. The proposed system is a sarcasm detection model which harness various features that characterize sarcasm in text like lexical, pragmatic, context incongruity, topic, and sentiment. Even though we use the context incongruity as the major feature for classification, our system can detect sarcastic tweets with and without context incongruity. Support Vector Machines (SVM) and Decision Tree are used for modeling the proposed system and both obtained promising results.