Sarcasm Detection in Text and Speech: A Comprehensive Study

Sarcasm is a prevalent form of communication characterized by a discrepancy between literal meaning and intended meaning. Detecting sarcasm presents a unique challenge, especially in the realm of Natural Language Processing (NLP) and speech analysis, due to the nuanced, context-dependent, and often ambiguous nature of sarcastic expressions. Sarcasm detection is unfolding area under sentiment analysis and text mining. It is preliminary need of all conversation Agent to detect sarcasm and create response based on natural language understanding. There are many developments since sentiment analysis is being attempted by many researchers. This paper addresses many of the attempts made for identification of Sarcasm detection for text and speech i.e., rule-based approach, Machine learning Approach and hybrid approach. Paper ends with proposed approach, various conclusions and areas where researchers can contribute in this area.