Sarcasm detection using machine learning algorithms in Twitter: A systematic review

Samer Muthana Sarsam, Hosam Al-Samarraie, Ahmed Ibrahim Alzahrani, Bianca Wright

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Abstract

Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted Machine Learning Algorithms (AMLA) and Customized Machine Learning Algorithms (CMLA). The review results revealed that Support Vector Machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.
Original languageEnglish
Pages (from-to)578-598
Number of pages21
JournalInternational Journal of Market Research
Volume62
Issue number5
Early online date28 May 2020
DOIs
Publication statusPublished - 1 Sep 2020

Bibliographical note

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Keywords

  • machine learning algorithms
  • sarcasm detection
  • trolling
  • twitter

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Marketing

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