随着主观性评价文本数量的不断增长,文本情感分析已经成为众多研究者关注的对象.比较要素抽取是比较句情感分析的重要研究任务之一,比较句的情感分析结果与比较要素相结合才更有意义.为了提高比较要素抽取的性能,本文提出在构建系统模型的过程中引入浅层句法信息、比较词候选信息和启发式位置信息等多种语言学相关特征,并且在不增加领域知识的情况下,有效提高系统的准确率和F1值,同时本文提出的方法可以有效处理含有多个比较关系的句子.实验结果表明,将本文提出的特征应用于条件随机域(Conditional random fields,CRFs)模型可以有效提高比较要素抽取的各项性能指标,同时,将本文的实验结果与2012年中文情感分析评测结果的最大值进行了比较,各项指标均超过最大值,进一步证明了本文方法的有效性.
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%.
