Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Abstract

With 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.

Sentiment analysis and sarcasm detection from social network to train health-care professionals

“…This selection of highly discriminating features among all features extracted also improves the performance of classification. Different techniques for selecting features are information gain, chi square, Gini index, gain ratio and so on (Kumar and Harish, 2019). Classification: Classification of sentiments refers to determining the polarity of the document/sentence/aspect towards an entity.…”

“…This selection of highly discriminating features among all features extracted also improves the performance of classification. Different techniques for selecting features are information gain, chi square, Gini index, gain ratio and so on (Kumar and Harish, 2019).…”

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

“…In literature, there is an enormous amount of machine learning applications where feature selection has been applied. Some of these applications involve medical diagnosis [4], facial expression recognition [5], diagnose of bronchitis [6], gene selection and cancer classification [7], image steganalysis [8], big data classification [9], obstructive sleep apnea diagnosis [10], sentiment classification [11], Mobile Agent Platform Protection [70], Irony Detection [71], categorize text documents [72], classification of Plant Diseases [73], Breast Masses Detection [74].…”

Editor’s Note

“…Kumar and Harish [7] present an article about sentiment analysis. Automatic detection of sarcasm or irony from content in microblogging reviews is a challenging task and the authors propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features.…”