Abstract

In today’s world there is need to develop a Sarcasm Detection model because of growth of social media. Due to rise in the usage of social media many people tend to write comments or posts that convey different meaning in one half and different meaning in other half, these types of sentences are known as sarcastic sentences. Now sarcasm detection is very useful in politics as well business because they tend to understand those text of the public and make changes in their way of handling their customers or followers. Sarcasm is frequently employed on social networks and microblogging platforms, where individuals use irony or criticism in a manner that can perplex even humans in determining whether the statement is sincere or not. Its figurative nature poses a significant hurdle for sentiment analysis. Although sarcasm carries an implicit negative sentiment, its outward expression often appears positive. The complexities surrounding sarcasm and the potential advantages of detecting sarcasm in the context of sentiment analysis have piqued interest in automatic sarcasm detection as a research endeavor. Automatic sarcasm detection encompasses computational techniques aimed at forecasting whether a provided text contains sarcasm. Sarcasm detection is a difficult task as it is even difficult for humans to understand a sarcastic text and it is even more difficult for a machine to understand a sarcastic text. It’s already difficult for a machine to understand a normal text and store it in a sequential order hence creating a sarcastic text detector is difficult, and there is not much review work done on this. This review paper focuses on different techniques that has been used till now, but our main task is to focus on the best machine learning models that can be used for sarcasm detection.