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AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

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Abstract

Sarcasm, though difficult to define but plays a crucial role in one’s life. Sarcasm as a jest is a matter of fun but when taken seriously can cause unwelcoming results. Sometimes, sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter jibe or taunt”. These days’ researchers are working towards the detection of sarcasm for the purpose of sentiment analysis. Emotion and sentiment-bearing information are carried by subjective sarcastic sentences. The objective of the paper is to highlight the different types of sarcastic tweets and their usage in sentiment analysis. The authors mainly emphasize several approaches which include sentiment analysis, machine and deep learning classifications. The paper focuses on the use of machine learning and deep learning for identifying sarcastic tweets. Numerous feature extraction techniques have been studied and machine and deep learning classifications have been taken into account. The comparative table shows the results obtained using the various evaluation metrics such as accuracy, precision, recall, and f-score.

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Authors and Affiliations

  1. Chandigarh Group of Colleges, Landran, Mohali, India

    Yogesh Kumar & Nikita Goel

  2. Maharaja Agrasen Institute of Technology, GGSIPU, Delhi, India

    Yogesh Kumar & Nikita Goel

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  1. Yogesh Kumar
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  2. Nikita Goel
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Correspondence to Yogesh Kumar.

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This article is part of the topical collection “Computational Statistics” guest edited by Anish Gupta, Mike Hinchey, Vincenzo Puri, Zeev Zalevsky and Wan Abdul Rahim.

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Kumar, Y., Goel, N. AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey. SN COMPUT. SCI. 1, 318 (2020). https://doi.org/10.1007/s42979-020-00336-3

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