Adding redundant features for CRFs-based sentence sentiment classification

Abstract

In this paper, we present a novel method based on CRF's in response to the two special characteristics of "contextual dependency" and "label redundancy" in sentence sentiment classification. We try to capture the contextual constraints on sentence sentiment using CRFs. Through introducing redundant labels into the original sentimental label set and organizing all labels into a hierarchy, our method can add redundant features into training for capturing the label redundancy. The experimental results prove that our method outperforms the traditional methods like NB, SVM, MaxEnt and standard chain CRFs. In comparison with the cascaded model, our method can effectively alleviate the error propagation among different layers and obtain better performance in each layer.