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, Haiming Wu

OAI: oai:igi-global.com:307904 • DOI: 10.4018/IJDWM.307904

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.

Data Mining Computer Science and Information Technology Data Mining and Databases