期刊论文

Xue, Yun

英文

Cluster Computing

1386-7857

2019

22

2

3043-3058

10.1007/s10586-018-1858-z

The authors thank gratefully for the colleagues participated in this work and provided technical supports. This work is supported by Grant from the National Natural Science Foundation of China (No. 61672126), Guangdong Provincial Engineering Technology Research Center for Data Science (Nos. 2016KF09, 2016KF10), and the National Statistical Science Research Project of China (No. 2016LY98). This work was also supported by the Science and Technology Department of Guangdong Province in China (Grant Nos. 2016A010101020, 2016A010101021, 2016A010101022), Foundation of Guangdong Polytechnic of Science and Technology (No. XJSC2016206), Natural Science Funds of Shenzhen Science and Technology Innovation Commission (No. JCYJ20160527172144272) and the Innovation Project of Graduate School of South China Normal University (No. 2015lkxm37). Furthermore, the authors thank gratefully for the scholars who shared datasets used in this work.

本校为其他机构

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 Ch...