A boosted SVM based ensemble classifier for sentiment analysis of online reviews

Abstract

In recent years, several approaches have been proposed for sentiment based classification of online text. Out of the different contemporary approaches, supervised machine learning techniques like Naive Bayes (NB) and Support Vector Machines (SVM) are found to be very effective, as reported in literature. However, some studies have reported that the conditional independence assumption of NB makes feature selection a crucial problem. Moreover, SVM also suffers from other issues like selection of kernel functions, skewed vector spaces and heterogeneity in the training examples. In this paper, we propose a hybrid method by integrating "weak" support vector machine classifiers using boosting techniques. The proposed model exploits classification performance of Boosting while using SVM as the base classifier, applied for sentiment based classification of online reviews. The results on movies and hotel review corpora of 2000 reviews have shown that the proposed approach has succeeded in improving the performance of SVM. The resultant ensemble classifier has performed better than the single base SVM classifier, and the results confirm that ensemble SVM with boosting, significantly outperforms single SVM in terms of accuracy.