中文摘要：

针对商品评论中的细粒度情感要素抽取问题，提出基于条件随机场模型（ CRFs）和支持向量机（ SVM）的层叠模型。针对情感对象与情感词的识别，将评论的句法信息、语义信息等引入CRFs模型，进一步提高CRFs特征模板的鲁棒性。在SVM模型中，引入情感对象和情感词的深层词义及情感词的基本情感倾向等特征，改进传统的词包模型，对掖情感对象，情感词业词对进行细粒度的情感分类判断，从而获得商品评论中的情感关键信息：（情感对象，情感词，情感倾向性）三元组。实验表明，文中的CRFs和SVM层叠模型可提高情感要素抽取与情感分类判断的准确性。

英文摘要：

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.