Sentiment analysis of Chinese online reviews using ensemble learning framework

Abstract

Unstructured online reviews are undergoing a rather rapid expansion with the development of E-commerce, and they contain sentiment information in which consumers and businesses are very interested. Therefore, effective sentiment classification has become one of the important research topics. Many studies have shown that ensemble learning methods may have great hopeful applicability in sentiment classification tasks. In this paper, we propose a new ensemble learning framework for sentiment classification of Chinese online reviews. First of all, according to the complicated characteristics of Chinese online reviews, we extract Part of Speech Combination Pattern, Frequent Word Sequence Pattern and Order Preserved Submatrix Pattern as the input features. Furthermore, we use the algorithm of Random Subspace based on Information Gain by considering the problem of massive features in the reviews, which can improve the base classifiers simultaneously. Finally, we adopt the algorithm of Constructing Base Classifiers based on Product Attributes to combine the sentiment information of each attribute in a review so as to obtain better performance on sentiment classification. The experimental results show that the proposed ensemble learning framework has significant improvement in sentiment classification of Chinese online reviews.