01944nas a2200265   4500000000100000000000100001008004100002260001200043653001900055653001500074653002200089653002100111653002300132653001900155653001000174653001200184100001500196700001800211245007500229856009900304300001000403490000600413520124500419022001401664       2019                            d  c12/201910aClassification10aClustering10aFeature Selection10aEnsemble Methods10aSentiment Analysis10aFeature Fusion10aIrony10aK-means1 aB S Harish1 aKeerthi Kumar00aAutomatic Irony Detection using Feature Fusion and Ensemble Classifier  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/07/ijimai20195_7_7_pdf_17438.pdf  a70-790 v53 aWith the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.  a1989-1660