A Systematic Review of the Sarcasm Detection in the Twitter Dataset
K. Veena1, V. Sasirekha2
1K. Veena, Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.-638183, Tamil Nadu, India.
2Dr. V. Sasirekha, Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.- 638183, Tamil Nadu, India.
Manuscript received on 21 December 2023 | Revised Manuscript received on 03 January 2024 | Manuscript Accepted on 15 January 2024 | Manuscript published on 30 January 2024. | PP: 26-33 | Volume-12 Issue-5, January 2024 | Retrieval Number: 100.1/ijrte.E798312050124 | DOI: 10.35940/ijrte.E7983.12050124
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Text is the most significant contributor to data generated on the Internet. Understanding a person’s opinion is a crucial aspect of natural language processing. However, people’s views can be skewed and inaccurate if they use sarcasm when posting status updates, commenting on blogs, and reviewing products and movies. Sarcasm detection has gained significant importance in social networking platforms because it can impact various applications, including sentiment analysis, opinion mining, and stance detection. Twitter is rapidly growing in volume, and its analysis presents significant challenges in detecting sarcasm. Our research focuses on various methodologies available for detecting sarcasm. Multiple papers from recent years were compiled, and a review was conducted. This paper discusses the literature on sarcasm detection, categorising datasets, preprocessing, feature extraction, feature selection, classification algorithms, and performance measures. This paper discusses the literature on sarcasm detection, categorising datasets, preprocessing, feature extraction, feature selection, classification algorithms, and performance measures. This work explores existing approaches, challenges, and future scopes for sarcasm detection in the Twitter dataset. This review highlights the analysis of sarcasm identification in Twitter data. It is intended to serve as a resource for researchers and practitioners interested in sarcasm detection and text classification.
Keywords: Sarcasm, Sentimental Analysis, Negative Connotation, Twitter, Machine Learning, Deep Learning
Scope of the Article: Computer Science and Its Applications