Bagged Ensemble Classifiers for Sentiment Classification of Movie Reviews (International Journal of Engineering and Computer Science, 2014)
The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. The feasibility and the benefits of the proposed approaches are demonstrated by means of movie review that is widely used in the field of sentiment classification. In the proposed work, a comparative study of the effectiveness of ensemble technique is made for sentiment classification. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on movie review data set in conjunction with Naive Bayes (NB), Support Vector Machine (SVM), Genetic Algorithm (GA) as the base learners. The proposed bagged NB, SVM, GA is superior to individual approaches for movie review in terms of classification accuracy.