Chinese sentiment classification based on stacking combination method

Abstract

情感文本分类(简称情感分类)是一种 面向主观信息分类的文本分类任务。目前,由于其广泛的应用前景,该任务在自然语言处理研究领域中得到了普遍关注,相继出现多种用于情感文本分类的有监督的 分类方法。该文具体研究四种不同的分类方法在中文情感分类上的应用,并且采用一种基于Stacking的组合分类方法,用以组合不同的分类方法。实验结果 表明,该组合方法在所有领域都能够获得比最好基分类方法更好的分类效果。从而克服了分类方法领域依赖的困境(不同领域需要选择不同基分类方法才能获得更好 的分类结果)。 Sentiment-based text categorization(for short,sentiment classification) is a task of classifying text according to the subjective information in the text.Nowadays,it has been closely studied in the research field of natural language processing(NLP) due to its wide real applications.As a result,many supervised machine learning classification approaches have been applied to this task.In this paper,we research on four classification approaches and propose a new combination method based on stacking to combine these four approaches.Experimental results show that our combination method achieves better performances than the best single one.Therefore,this combination method can avoid selecting a suitable classification approach according to different domains.