Sarcasm Detection using Random Forest and Naive Bayes Classifiers

Abstract

Sarcasm could be a linguistic development during which individuals state the other of what they really mean. It is a vicinity of opinion mining that is studied so that the various sentiments will be analyzed and worked upon. Sarcasm detection could be a difficult task, even for humans whereas while speaking with each other different body languages are involved due to which it becomes easy to identify sarcasm in the statement. However, just in case of the text information, this body languages are missing thus the sarcasm detection task becomes complicated or difficult. Because the use of social media is inflated the degree of knowledge being generated is growing drastically. With such high volumes of knowledge being generated variety of challenges additionally comes into image. In this work, the text is used to determine the sarcasm. Firstly, the text is preprocessed then the feature choice is employed to mechanically or manually choose those options that contribute most to our prediction variable. Nextly, the classification is completed during which Random Forest, Naïve Bayes classification algorithm will be used to classify the text. Primarily, based upon whether or not the statement contains the positive or negative words the sarcasm detection procedure will be carried out.