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Published January 30, 2024 | Version CC BY-NC-ND 4.0
Journal article Open

A Systematic Review of the Sarcasm Detection in the Twitter Dataset

Creators

  • 1. Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.-638183, Tamil Nadu, India.

Contributors

Contact person:

  • 1. Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.-638183, Tamil Nadu, India.

Description

Abstract: Text is the most significant contributor to data generated on the Internet. Understanding a person's opinion is an essential part of natural language processing. However, people's views can be skewed and inaccurate if people use sarcasm when they post status updates, comment on blogs, and review products and movies. Sarcasm detection has gained an important role in social networking platforms because it can impact many applications such as sentimental analysis, opinion mining, and stance detection. Twitter is rapidly growing in volume, and its analysis presents significant challenges in detecting sarcasm. Our research work focuses on various methodologies available for detection of sarcasm. Various papers from recent years were collected and review was carried out. This paper discusses the literature on sarcasm detection under the category of datasets, in different pre-processing, feature extraction, feature selection, classification algorithms, and performance measures. This paper discusses the literature on sarcasm detection under the category of datasets, in different pre-processing, 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 brings to light the analysis of sarcasm identification in Twitter data and is intended to serve as a resource for researchers and practitioners interested in sarcasm detection and text classification.

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Additional details

Identifiers

EISSN
2277-3878
DOI
10.35940/ijrte.E7983.12050124

Dates

Accepted
2024-01-15
Manuscript received on 21 December 2023 | RevisedManuscript received on 03 January 2024 | Manuscript Accepted on 15 January 2024 | Manuscript published on 30 January 2024

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