Scaling feature selection method for enhancing the classification performance of Support Vector Machines in text mining

Abstract

Abstract The classification of opinion based on customer reviews is a complex process owing to high dimensionality. In this study, our objective is to select the minimum number of features to effectively classify reviews. The tf-idf and Glasgow methods are commonly for feature selection in opinion mining. We propose two modifications to the traditional tf-idf and Glasgow expressions using graphical representations to reduce the size of the feature set. The accuracy of the proposed expressions is established through the support vector machine technique. In addition, a new framework is devised to measure the effectiveness of the term weighting expressions adopted for feature selection. Finally, the strength of the expressions is established through evaluation criteria and effectiveness, and this strength is tested statistically. Based on our experimental results, our modified tf-idf and Glasgow methods performed better than the traditional term weighting expressions for the extraction of the minimum number of prominent features required for classification, thus enhancing the performance of the Support Vector Machine.