Effective Analysis of Emotion-Based Satire Detection Model on Various Machine Learning Algorithms

Even though the various features of satirical language have been studied in computational linguistics, most of the research works have relied on the performance of the single machine learning algorithm. However, the implicit traits embedded in the language demand more certain, precise and accurate combination powers of an individual algorithm. In this study, we analyzed the performance of emotion-based satire detection model on various machine learning algorithms: Regression, Naive Bayes, SVM and ensemble classifiers. Experiments on shifting base classifiers to ensemble classifiers demonstrate that ambiguous and implicit nature of satirical emotions can lead to the misclassification accuracy while implementing the base classifiers but, offer reliable classification accuracy with ensemble classifiers.