Predicting Opinion Dependency Relations for Opinion Analysis

Syntactic structures have been good features for opinion analysis, but it is not easy to use them. To find these features by supervised learning methods, correct syntactic labels are indispensible. Two possible sources to acquire syntactic structures are parsing trees and dependency trees. For the annotation processing, parsing trees are more readable for annotators, while dependency trees are easier to use by programs. To use syntactic structures as features, this paper tried to annotate on human friendly materials and transform these annotations to the corresponding machine friendly materials. We annotated the gold answers of opinion syntactic structures on the parsing tree from Chinese Treebank, and then proposed methods to find their corresponding dependency relations on the dependency trees generated from the same sentence. With these relations, we could train a model to annotate opinion dependency relations automatically to provide an opinion dependency parser, which is language independent if language resources are incorporated. Experiment results show that the annotated syntactic structures and their corresponding dependency relations improve at least 8% of the performance of opinion analysis.