CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media

Abstract

Text processing is an important task in various machine learning applications. One among the applications is Sentiment analysis. However, the presence of sarcasm makes it difficult for analyzing the sentiment of the statement. In the current scenario, the amount of sarcastic statements in any social media platform is high taking the forms of memes, comments, trolls etc. Hence it is important to identify sarcasm to preserve the polarity of any given statement. Sarcasm usually means the opposite of what the sentence seems to convey. While the existing works in literature have focused on detecting sarcasm, the proposed model, in addition to that, determines the levels of sarcasm present in the text, which will aid in finding the level of harshness present in the statement. In this work, an unsupervised learning model, Conditional Random Field model based Modified Expectation Maximization (CRF-MEM) algorithm has been proposed for detecting sarcasm in tweets. The proposed model aims to overcome the limitation present in the traditional EM algorithm, the random assignment factor, with the proposed aspect relationship value. Experimental results showed that the proposed CRF-MEM achieved an accuracy of 91.89% whereas the traditional EM displayed an accuracy of 80% in detecting sarcasm from text.