Ensemble Classification Approach for Sarcasm Detection. (22nd November 2021)

• Record Type: Journal Article
• Authors: Godara, Jyoti; Batra, Isha; Aron, Rajni; Shabaz, Mohammad
• Other Names: Lin Hong Academic Editor.
• Is Part Of: Behavioural neurology. Volume 2021(2021)
• Publication Date: 2021-11-22
• Subjects: Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; Cognitive neuroscience -- Periodicals; 616.8005
• DOI: 10.1155/2021/9731519
• Languages: English
• ISSNs: 0953-4180

Abstract

Abstract : Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.