An intelligent machine learning-based sarcasm detection and classification model on social networks

Abstract

Due to the exponential increase in Internet usage, sarcasm detection has gained significant attention in online social networking platforms. Sarcasm is a linguistic expression of dislikes or negative emotions by the use of overstated language constructs. Because of the complex nature and ambiguities of sarcasm, sarcasm detection becomes an NLP process and is commonly employed in sentiment analysis, human–machine dialogue, and other NLP applications. At the same time, the advent of Machine learning (ML) algorithms paves a way to design effective sarcasm detection approaches. In this aspect, this paper presents an Intelligent ML-based sarcasm detection and classification (IMLB-SDC) technique. The goal of the IMLB-SDC model is to detect the existence of sarcasm in social media. The IMLB-SDC model involves different stages of operations such as preprocessing, feature engineering, Feature selection (FS), classification, and parameter tuning. Besides, feature engineering process takes place using Term frequency—inverse document frequency (TF-IDF). In addition, two Feature selection (FS) approaches are utilized, namely chi-square and information gain. The IMLB-SDC model involves the Support vector machine (SVM) as a classification model, and the penalty factor $C$ can be optimally tuned by the use of Particle swarm optimization (PSO) algorithm. A wide range of experiments takes place to ensure the improved performance of the IMLB-SDC technique. The experimental outcomes pointed out the promising efficiency of the IMLB-SDC technique over the recent state-of-the-art techniques with the precision, recall, and F-score of 0.947, 0.952, and 0.949, respectively.