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Data Science and Intelligent Applications pp 247–259Cite as

A Survey on Machine Learning and Deep Learning Based Approaches for Sarcasm Identification in Social Media

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 52))

Abstract

Today, sentiment analysis is a basic way through which one can get an idea regarding opinion, attitude and emotion towards person or aspects or products or services, etc. In the last several years, researchers are working on a technique to analyse social media data and social chain to identify the undisclosed information from it and to make meaningful patterns and decisions through it. In sentiment analysis, sarcasm is one kind of emotion which contains the contents that are opposite of what you really want to say. People use it to show disrespect or to taunt someone. Sarcasm is useful to show silliness and to be entertaining. Sarcasm can be expressed verbally or through certain gestural clues like rolling of the eyes or raising the eyebrows, etc. A number of ways are implemented to detect sarcasm. In this paper, we try to explain current and trending ways which are used to detect sarcasm.

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Authors and Affiliations

  1. Gandhinagar Institute of Technology, Kalol, Gujarat, 382721, India

    Bhumi Shah & Margil Shah

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  1. Bhumi Shah
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  2. Margil Shah
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Correspondence to Bhumi Shah .

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Editors and Affiliations

  1. Faculty of Engineering, Symbiosis Institute of Technology, Pune, India

    Ketan Kotecha

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Vincenzo Piuri

  3. Gandhinagar Institute of Technology, Gandhinagar, Gujarat, India

    Hetalkumar N. Shah

  4. Department of Computer Engineering, Gandhinagar Institute of Technology, Gandhinagar, Gujarat, India

    Rajan Patel

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Shah, B., Shah, M. (2021). A Survey on Machine Learning and Deep Learning Based Approaches for Sarcasm Identification in Social Media. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_29

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