基于层叠模型细粒度情感要素抽取及倾向分析<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201506005

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Abstract For the fine-grained emotional elements extraction problem in product reviews,a cascaded model combining conditional random fields (CRFs) and support vector machine (SVM) is put forward. Aiming at the recognition of sentiment objects and emotional words, the review of syntactic and semantic informations are introduced into CRFs model to further improve the robustness of feature templates in CRFs. In SVM model, the features of deep semantic information of sentiment objects and emotional words and basic emotional orientation of emotional words are introduced to improve the traditional bag-of-words model. The sentiment of <sentiment object, emotional word> word pair is classified to acquire key information from product reviews, namely triples of (sentiment object, sentiment word, sentiment trend). Experimental results show that the proposed CRFs and SVM cascaded model efficiently improves the precision of emotional elements extraction and emotion classification.

Key words： Affective Computing Emotional Element Semantic Role Syntax Dependency Tree Meaning Clustering

Received: 24 March 2014

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	SUN Xiao
	TANG Chen-Yi

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SUN Xiao,TANG Chen-Yi. Fine-Grained Emotional Elements Extraction and Affection Analysis Based on Cascaded Model[J]. , 2015, 28(6): 513-520.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201506005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2015/V28/I6/513

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