A Large Scale Study for Identification of Sarcasm in Textual Data

  • Pulkit MehndirattaEmail author
  • Devpriya Soni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 922)


With the increase in the penetration of the Internet and the widespread acceptability of social networking sites, more people are coming forward to express their views and opinion about various topics. This has given a huge boost to the textual and multimedia content generated by these websites, giving opportunities to researchers and analysts to nd and generate patterns from this data. The problem of identification of sarcasm in textual data is quite challenging due to lack of annotation, intonation and facial expression. Big companies are spending millions in finding out, whether people were praising or mocking about their product, they can get the idea about the market trends and needs. Law enforcement agencies may also get benefit from this as they would be able to distinguish legitimate threats from exaggerations on the online social networks. A data-driven approach based on the neural networks and the concepts of deep learning has been evaluated using a blend of deep convolutional networks (CNN) and long short term memory (LSTM). The technique has been applied to the Self-Annotated Reddit Corpus (SARC) (, a large corpus for sarcasm research. The technique for domain specific and general data is also probed, as given in the dataset so as to check the accuracy of the proposed method. It has been observed that blending of the models further improves the accuracy of simple CNN model, and yields a more computationally efficient model of accuracy compared to standalone models. Our method has achieved an overall average precision of 73%.


Online social networks Opinion mining and sentiment analysis Text analysis Irony and sarcasm Deep learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

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