Title: A comprehensive review of automated sarcasm detection techniques on Twitter

Authors: Anindya Nag; Md. Mehedi Hassan; Ayontika Das; Anurag Sinha; Moupriya Das; G. Madhukar Rao; Md. Shahin Ali; Chetna Kaushal; Biresh Kumar; Neetu Singh

Addresses: Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh ' Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh ' Computer Science and Engineering, Adamas University, Kolkata, India ' Department of Computer Science, IGNOU, New Delhi, India ' Computer Science and Engineering, Adamas University, Kolkata, India ' Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Hyderabad, 500075, India ' Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh ' Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India ' Department of Computer science and Engineering, Amity University, Jharkhand, India ' Department of Information Technology, Bharati Vidyapeeth's College of Engineering, Paschim Vihar Delhi 110063, India

Abstract: Automated sarcasm recognition is a valuable tool for analysing user sentiment on social media platforms like Twitter and Facebook. Sarcasm, a form of criticism, involves expressing thoughts contrary to the intended meaning, often as a means of insult or humour. Failing to recognise sarcasm can limit social and professional interactions. Our approach for sarcasm recognition utilises user embedding fitting, requiring only the user's previous text posts without complex feature engineering or simultaneous data harvesting. In comparison to a state-of-the-art strategy relying on an extensive attribute collection, our model demonstrates superior performance. We evaluated various classifiers, including ARABERT, SVM, LSTMs, CNN, and Pattern-based methods, on Twitter sarcasm detection. CNN achieved the highest accuracy at 95%, followed by LSTMs at 91.60%.

Keywords: social media; sarcasm detection; Twitter; automatic sarcasm detection; Twitter data collection.

DOI: 10.1504/IJSEM.2026.152444

International Journal of Services, Economics and Management, 2026 Vol.17 No.2, pp.181 - 203

Received: 11 Jun 2023
Accepted: 22 Oct 2023
Published online: 20 Mar 2026 *

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