CAJ | 학술논문

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。
침대상품평론중적세립도정감요소추취문제，제출기우조건수궤장모형( CRFs)화지지향량궤( SVM)적층첩모형。침대정감대상여정감사적식별，장평론적구법신식、어의신식등인입CRFs모형，진일보제고CRFs특정모판적로봉성。재SVM모형중，인입정감대상화정감사적심층사의급정감사적기본정감경향등특정，개진전통적사포모형，대액정감대상，정감사업사대진행세립도적정감분류판단，종이획득상품평론중적정감관건신식：(정감대상，정감사，정감경향성)삼원조。실험표명，문중적CRFs화SVM층첩모형가제고정감요소추취여정감분류판단적준학성。
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.