Evolution and Evaluation: Sarcasm Analysis for Twitter Data Using Sentiment Analysis

Abstract

This paper addresses the evolution and evaluation of sarcasm in textual form. The growing popularity of social networking sites is well known, and every individual generates a whole new set of opinions in form of blogs, microposts, etc. Sentiment analysis is one of the fastest evolving aspects of artificial intelligence categorizing opinions under positive, negative, or neutral sentiments. One such part of sentiment analysis is sarcasm. Sarcasm is becoming a common phenomenon in networking sites where expressing murky feelings wrapped by positive words for conveying contempt is highly used, making it difficult to understand the actual meaning of a statement. When reading customer reviews or complaints, it might be helpful to understand the consumers’ genuine intentions in order to enhance the efficiency of customer support or after-sales services. In this paper, different classifiers—decision tree, Naïve Bayes, k-nearest, and support vector machine are used to predict a statement under the category sarcastic or nonsarcastic using tweeter data; the following proposed methodology is used for the experimental evaluation concluding that the given classifiers SVM gains the highest accuracy of 93%, whereas Naïve Bayes and decision tree are performing well with an accuracy of 83% and 86%, respectively, along with the lowest of 51% attained by KNN.