A Pragmatic Study on Various Approaches Used for Sarcasm Detection

Sarcasm is an intricate type of figurative language that presents a significant obstacle to sentiment analysis because it skillfully conveys negative feelings through words that appear positive or intensified, making classification more difficult. Sentiment analysis works well with plain language, but sarcasm is difficult to interpret precisely because of its complex structure. To tackle this problem, different approaches are used, such as integrating language cues into conventional techniques and utilizing deep learning's ability to extract complex patterns from large-scale datasets. New complexities are introduced by the rapidly changing digital language landscape, which calls for constant model adaptation to take into account dynamic expressions, slang that is still developing, and changing cultural contexts. This review paper offers an overview of previous works on automatic sarcasm detection, providing information on approaches, datasets, problems, and patterns in the field. It gives a thorough analysis of rule-based, deep learning, machine learning, and conventional methods, leveraging their distinct advantages to improve sarcasm detection. Additionally, the paper presents indicative performance metrics, discusses common tasks, and makes recommendations for future research.