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清华大学学报(自然科学版)  2017, Vol. 57 Issue (9): 926-931    DOI: 10.16511/j.cnki.qhdxxb.2017.26.042
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于联合模型的商品口碑数据情感挖掘
王素格1,2, 李大宇1, 李旸1
1. 山西大学 计算机与信息技术学院, 太原 030006;
2. 山西大学 计算智能与中文信息处理教育部重点实验室, 太原 030006
Sentiment mining of commodity reputation data based on joint model
WANG Suge1,2, LI Dayu1, LI Yang1
1. School of Computer & Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
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摘要 为了同时挖掘商品口碑数据中所谈论的对象、对象的某个方面以及评论者对这个方面的观点,用于指导消费者消费和生产厂家对商品的改进,该文面向口碑数据提出一个无监督对象方面情感联合模型。该模型假设方面分布依赖于对象分布,情感分布依赖于方面分布和对象分布,词是采样的最小单位。在汽车口碑数据上进行了多组实验,实验结果表明:无监督对象方面情感联合模型不仅可以判别文本方面和文本情感的类别,还可以获取文本对象信息。
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王素格
李大宇
李旸
关键词 口碑数据联合模型情感挖掘无监督学习    
Abstract:This paper presents a probabilistic graphical model to simultaneously extract objects, aspects and sentiments from commodity reputation data. The underlying assumption is that the aspect distribution depends on the object distribution, while the sentiment distribution depends on the aspect distribution. The model further assumes that words are the smallest sampling units and is fully unsupervised. Tests on car reputation data show that this model can predict the aspect and sentiment categories of commodity reviews and can simultaneously extract object information from the reviews.
Key wordscommodity reputation data    joint model    sentiment mining    unsupervised learning
收稿日期: 2016-12-06      出版日期: 2017-09-15
ZTFLH:  TP391.1  
引用本文:   
王素格, 李大宇, 李旸. 基于联合模型的商品口碑数据情感挖掘[J]. 清华大学学报(自然科学版), 2017, 57(9): 926-931.
WANG Suge, LI Dayu, LI Yang. Sentiment mining of commodity reputation data based on joint model. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 926-931.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.26.042  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I9/926
  图1 JOAS模型
  图2 不同迭代次数下的文本情感分类正确率
  表1 在不同参数下的文本对象分类、方面分类和情感分类及其联合分类正确率
  表2 方面与情感对应的词汇表
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