Opinion classification with tree kernel SVM using linguistic modality analysis

要旨

We propose a method for classifying opinions which captures the role of linguistic modalities in the sentence. We use features than simple bag-of-words or opinion-holding predicates. The method is based on a machine learning and utilizes opinion-holding predicates and linguistic modalities as features. Two different detectors help to classify the opinions: the opinion-holding predicate detector and the modality detector. An opinion in the target is first parsed into a dependency structure, and then the opinion-holding predicates and modalities stick onto the leaf nodes of the dependency tree. The whole tree is regarded as input features of the opinion, and it becomes the input of tree kernel support vector machines. We have applied method to opinions in Japanese about television programs, and have confirmed the effectiveness of the method against conventional bag-of-words features, or against simple opinion-holding predicates features

主題