A hybrid approach to sentiment classification and feature expansion strategy

A hybrid approach to sentiment classification was proposed, and an efficient strategy of incorporating dependency features was presented, which increases the accuracy of the system and avoids the defects of increased computing volume brought by the traditional feature expansion method.

Abstract

In this paper,focusing on sentiment text classification,the performance of generative and discriminative models for sentiment classification was studied,and a hybrid approach to sentiment classification was proposed.The individual generative classifier(naive Bayes,(NB) and the discriminative classifier(support vector machines,SVM) were merged into a hybrid version in a two-stage process in order to overcome individual drawbacks and benefit from the merits of both systems.On the basis of the hybrid classifier,an efficient strategy of incorporating dependency features was also presented.The strategy not only increases the accuracy of the system,but also avoids the defects of increased computing volume brought by the traditional feature expansion method.Experimental results show the apparent advantages of this approach in both classification accuracy and efficiency.