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Sarcasm Analysis on Twitter Data Using Machine Learning Approaches

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Trends in Social Network Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Sarcasm analysis, being one of the toughest challenges in natural language processing (NLP), has become a hot topic of research these days. A lot of work has already been done in the field of sentiment analysis, but there are huge challenges still being faced in identification of sarcasm. The property of sarcasm that makes it difficult to analyze and detect is the gap between its literal and intended meaning. Detecting sentiment in social media like Facebook, Twitter, online blogs, and reviews has become an essential task as they influence every business organization. In this chapter, four approaches were proposed, namely parsing-based lexical generation algorithm, likes and dislikes contradiction, tweet contradicting universal facts, and tweet contradicting temporary facts. The aim of the proposed methods is to extract text features such as lexical, hyperbole, behavioral, and universal facts. Further, four machine learning classifiers, namely support vector machine, Naive Bayes, maximum entropy, and decision tree, were deployed. Finally, we trained these classifiers using an extracted feature set to identify sarcasm in Twitter data. This work attains a considerable accuracy improvement over existing techniques.

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Authors and Affiliations

  1. National Institute of Technology Rourkela, Rourkela, India

    Santosh Kumar Bharti, Ramkrushna Pradhan, Korra Sathya Babu & Sanjay Kumar Jena

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  1. Santosh Kumar Bharti
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  2. Ramkrushna Pradhan
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  3. Korra Sathya Babu
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  4. Sanjay Kumar Jena
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Correspondence to Santosh Kumar Bharti .

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Editors and Affiliations

  1. Department of Computer Science & Engineering, University of Quebec in Outaouais, Gatineau, QC, Canada

    Rokia Missaoui

  2. Department of Computer Science and Networks, Telecom ParisTech, Paris, France

    Talel Abdessalem

  3. UPMC Univ Paris 06, CNRS LIP6 UMR 7606, Sorbonne Universitès, Paris, France

    Matthieu Latapy

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Bharti, S.K., Pradhan, R., Babu, K.S., Jena, S.K. (2017). Sarcasm Analysis on Twitter Data Using Machine Learning Approaches. In: Missaoui, R., Abdessalem, T., Latapy, M. (eds) Trends in Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-53420-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-53420-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53419-0

  • Online ISBN: 978-3-319-53420-6

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