基于评价对象类别的跨领域情感分类方法研究

情感分类任务具有领域相关性,即使用某一个领域的标注样本训练出的分类模型在对其他领域样本进行分类时性能表现往往会非常差。情感分类的跨领域学习旨在减少跨领域的性能损失。提出一种基于评价对象类别的跨领域学习方法。首先,将评价对象分为4大类:整体、硬件、软件和服务;然后,人工标注源领域中属于以上4类评价对象的句子,并构建评价对象类别分类器;最后,将不同的评价对象类别当作不同的视图,进而使用协同学习(Co-trai-ning)进行跨领域情感分类。实验结果表明,提出的方法有效地改进了跨领域学习性能。

Cross-domain Sentiment Classification with Opinion Target Categorizati nn

The task of sentiment classification is domain-specific,i. e. ,a classifier learning from the annotated data from a domain often performs dramatically badly on the data from a different domain. We presented a novel approach for cross-domain sentiment classification. Specifically, we first generalized four general categories of the opinion targets; o- verall, software, hardware, and service and classified all sentences into these categories. hhen, some sentences with the category information were annotated in the source domain and a classifier for opinion target categorization was developed with the annotated data to classify all the sentences in both the source and target domain. Third, the four categories of opinion targets were considered as four different views which arc employed in a standard co-training algorithm to per- form cross-domain sentiment classification. Experimental results across several domains of Chinese reviews demonstrate the effectiveness of the proposed approach.