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This research presents a feature selection problem for classification of sentiments that uses ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and forest optimization algorithm (FOA) (i.e., mRMR-FOA)-based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publicly available on UCI machine learning repository. The classifiers for example k-nearest neighbor (k-NN), support vector machine (SVM), and naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has improved by 12-18%. The evaluated results are further enhanced by the ensemble of k-NN, NB, and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)