Paper: Structural Opinion Mining for Graph-based Sentiment Representation

ACL ID D11-1123
Title Structural Opinion Mining for Graph-based Sentiment Representation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Based on analysis of on-line review corpus we observe that most sentences have compli- cated 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 rep- resentation 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 la- beled Chinese corpus demonstrate the effec- tiveness of the proposed approach.