Structural Opinion Mining for Graph-based Sentiment Representation

Yuanbin Wu, Qi Zhang, Xuanjing Huang, Lide Wu

Abstract: Based on analysis of on-line review corpus we observe that most sentences have complicated opinion structures and they cannot be well represented by existing methods, such as frame-based and feature-based ones. In this work, we propose a novel graph-based representation for sentence level sentiment. An integer linear programming-based structural learning method is then introduced to produce the graph representations of input sentences. Experimental evaluations on a manually labeled Chinese corpus demonstrate the effectiveness of the proposed approach.