基于情感标签的极性分类

Polarity Classification Based on Sentiment Tags

情感极性分析是文本挖掘中一种非常重要的技术.然而在不同领域中,很多情感极性分类系统存在分类精度低和缺少大量标注数据的缺陷.针对这些问题,提出了一种基于情感标签的极性分类方法.首先通过所有文本建立Sentiment-Topic模型,抽取出文本的情感标签;然后利用情感标签将文本划分为两个子文本,并通过Co-training算法对子文本进行分类;最后合并两个子文本的分类结果,并确定文本的情感极性.实验结果表明该方法具有较高的分类精度,而且不需要大量的分类样本.

Sentiment analysis is a very important technology in text mining. However,a number of systems require amounts of annotated training data in different fields. In order to solve these problems, an approach to polarity classification based on sentiment tags is proposed. Firstly,o n the basis of all the documents, the sentiment-topic model is developed and the sentiment tags for each review are extracted. Then each review is divided into two sub-texts by these sentiment tags, and each sub-text is classified by exploiting the co-training algorithm. Finally, the category results of two sub-texts are combined to determine document-level polarity of each review. Experimental results show that compared with other algorithms, the method improves the classification precision without a large number of annotated samples.