【摘要】 该文研究属性依赖情感知识学习。首先提出了一个新颖的话题模型,属性观点联合模型(Joint Aspect/Opinion model,JAO),来同时抽取评论实体属性及属性相关观点词信息。在此基础上,对于各个属性,构造属性依赖的词关系图,并在该图上应用马尔科夫随机行走过程来计算观点词到少量褒、贬种子词的游走时间(Hitting Time),进而估计这些词的属性依赖的情感极性分值。在餐馆点评数据上的实验表明所提出的方法能有效抽取属性相关观点词,同时有效估计其属性依赖的情感极性分值。更多还原

【Abstract】 This paper addresses the problem of learning aspect-dependent sentiment knowledge.Specifically,a novel topic model,called Joint Aspect/Opinion Model(JAO),is proposed to detect aspects and aspect-specific opinion words simultaneoasly in an unsupervised manner.Then,we propose to infer aspect-dependent sentiment polarity scores for these opinion words based on the hitting times from the words to a handful of positive/negative seed words,by applying Markov random walks over an aspect-specific word relation graph.Experimental results on restaurant review data show the effectiveness of the proposed approaches.更多还原