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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.

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