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With the ever increasing acceptance of Online Social Networks (OSNs), a new dimension has evolved for communication amongst humans. People now share continuous streams of messages to contribute their interests, indications, discuss activities, status, latest trends and much more. This gives an opportunity to monitor and mine the opinions of a large number of online active population in real time. On one side where researchers can find out a pattern to judge the mood of the user, a serious problem of detection of irony and sarcasm in textual data poses threat to the accuracy of the techniques evolved till date. In this paper, we are proposing a deep learning based convolutional neural network method that focuses on the skip gram technique to convert words to vectors and further to detect and identify the sarcasm and irony from the normal polarized text taken from Twitter. We try to discover patterns from the textual datasets based on the identifiable features. Our approach is giving an overall accuracy of 89.9%.
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