Online forum post opinion classification based on tree conditional random fields model

Abstract

There is a major defect when using the traditional topic-opinion model for post opinion classifications in an online forum discussion. The accuracy of the classification based on the topic-opinion model highly depends on the observable topic-opinion features aiming at the subject, while a large number of posts do not have such features in a forum. Therefore, for the most part, the accuracy is less than 78%. To solve this problem, we propose a new method to identify post opinions based on the Tree Conditional Random Fields (T-CRFs) model. First, we select the topic-opinion features of the posts and associated opinion features between posts to construct the T-CRFs model, and then we use the T-CRFs model to label the opinions of the tree-structured posts under the same topic iteratively to reach a maximum joint probability. To reduce the training cost, we design a simplified tree diagram module and some feature templates. Experimental results suggest the proposed method costs less training time and improves the accuracy by 11%.