<![CDATA[Sarcasm detection in text is an inspiring field to explore due to its contradictory behavior. Textual data can be analyzed in order to discover clues those lead to sarcasm. A Deep learning-based framework is applied in this paper in order to extract sarcastic clues automatically from text data. In this context, twitter news dataset is exploited to recognize sarcasm. Convolutional-Recurrent Neural network (C-RNN) based model is proposed in this paper that enables automatic discovery of sarcastic pattern detection. The proposed model consists of two major layers such as convolutional layer, and Long-term short memory (LSTM) layers. LSTM is known to be a variant of traditional RNN. Experimental results confirmed sarcastic news detection with promising accuracy of 84.73%. This research work exhibits its uniqueness in combining two dissimilar Deep Learning frameworks under a single entity for predicting sarcastic posts.]]>
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