Unfolding Sarcasm in Twitter Using C-RNN Approach

Shawni Dutta; Akash Mehta

doi:10.25008/bcsee.v2i1.1134

Authors

Shawni Dutta Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India https://orcid.org/0000-0001-8557-0376
Akash Mehta Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India https://orcid.org/0000-0001-5709-849X

DOI:

https://doi.org/10.25008/bcsee.v2i1.1134

Keywords:

Sarcasm Detection, Deep Learning, CNN, RNN, CNN-LSTM, Twitter

Abstract

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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