Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering

Xin Lu, Donghong Gu, Haolan Zhang, Zhengxin Song, Qianhua Cai, Hongya Zhao and Haiming Wu
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Xin Lu: School of Electronics and Information Engineering, South China Normal University, China
Donghong Gu: School of Electronics and Information Engineering, South China Normal University, China
Haolan Zhang: Ningbo Institute of Technology, Zhejiang University, China
Zhengxin Song: School of Electronics and Information Engineering, South China Normal University, China
Qianhua Cai: School of Electronics and Information Engineering, South China Normal University, China
Hongya Zhao: Shenzhen Polytechnic, China
Haiming Wu: School of Electronics and Information Engineering, South China Normal University, China

International Journal of Data Warehousing and Mining (IJDWM), 2022, vol. 18, issue 1, 1-20

Abstract: Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.

Date: 2022
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