| 面向在线顾客点评的属性依赖情感知识学习 |
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| 引用本文： | 徐学可,谭松波,刘悦,程学旗,吴琼.面向在线顾客点评的属性依赖情感知识学习[J].中文信息学报,2015,29(3):121-129. |
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| 作者姓名： | 徐学可 谭松波 刘悦 程学旗 吴琼 |
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| 作者单位： | 1. 中国科学院计算技术研究所,网络数据科学与技术重点实验室,北京 100190;2. 中国科学院大学,北京 100190 |
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| 基金项目： | 国家高技术研究发展计划(863计划)项目，国家自然科学基金资助项目 |
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| 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。
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| 关 键 词： | 顾客点评 属性观点联合模型 游走时间 属性依赖情感知识 |
Learning Aspect-Dependent Sentiment Knowledge for Online Customer Reviews |
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| XU Xueke,TAN Songbo,LIU Yue,CHENG Xueqi,WU Qiong.Learning Aspect-Dependent Sentiment Knowledge for Online Customer Reviews[J].Journal of Chinese Information Processing,2015,29(3):121-129. |
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| Authors: | XU Xueke TAN Songbo LIU Yue CHENG Xueqi WU Qiong |
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| Affiliation: | 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academic of Sciences, Beijing 100190, China |
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| 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. |
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| Keywords: | online customer review joint aspect/opinion model hitting time aspect-dependent sentiment knowledge |
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