摘要
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%.
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%.
基金
supported by the National Natural Science Foundation of China under Grant No. 60873246
China Information Technology Security Evaluation Centre
