Abstract: In the course of the most recent couple of decades, Social networking and micro blogging websites such as twitter has allowed people to encounter in utilizing online assets. Twitter is rapidly gaining popularity as they allow the users to express and also share their opiniosns about certain related topics, which have been a mode for discussion with different communities, and messages are being posted across the world via this Social networking medium. A great deal of improvements has been observed in the field of sentimental analysis of twitter data. This project focuses mainly on sarcasm detection which is a major part of sentiment analysis of twitter data which is helpful to analyze the sarcasm in the tweets where views are miscellaneous and highly unstructured, or may be positive, negative, sarcastic, ironic or neutral in some cases. This research work borrows the ideas of utilizing different semi-supervised algorithms like Lexical Analysis with N-grams approach, Knowledge extraction, contrast approach, emoticon based approach and hyperbole approach to propose a new rule based Hybrid approach for sarcasm detection.