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.
Highlights
Microblogging sites provide an open stage to a common individual to convey their thoughts, views, and opinions on different subjects and episodes
In the second ensemble classifier (SLD), Support vector machine (SVM), logistic regression, and decision tree classifiers are combined for the sarcasm detection
The data is preprocessed using approach of tokenization; the features are extracted using random forest algorithm; Principal component analysis (PCA) algorithm is applied for the feature reduction; K-means is used for the data clustering; and in the phase of classification, four different ensemble classifiers are designed which are a combination of multiple classifiers
Summary
Microblogging sites provide an open stage to a common individual to convey their thoughts, views, and opinions on different subjects and episodes. Sarcasm is a complex version of irony generally observed in social media and microblogging websites, as these media usually promote trolling and/ or condemnation of others. As a word, usually expresses verbal irony. Opinion mining and reputation management find automatic sarcasm detection quite advantageous. It is a daunting task to deal with text on social network. Text available on social networking sites is misspelled and contained abbreviations, slang, etc. Figurative linguistic is conveyed very briefly, which generates one more issue. Individuals expressing their opinions with sarcastic words are free to select the language form to meet their interaction objectives. The major goal of the work of detecting sarcasm is to find the characteristics that enable people to distinguish satirical texts from nonsatirical texts [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
