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现代图书情报技术  2014, Vol. 30 Issue (9): 81-90    DOI: 10.11925/infotech.1003-3513.2014.09.11
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
基于评论产品属性情感倾向评估的虚假评论识别研究
陈燕方1,2, 李志宇1,2,3
1. 华中师范大学信息管理学院 武汉 430079;
2. 华中师范大学湖北省电子商务研究中心 武汉 430079;
3. 中国人民大学信息学院 北京 100872
Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection
Chen Yanfang1,2, Li Zhiyu1,2,3
1. School of Information Management, Central China Normal University, Wuhan 430079, China;
2. E-Commerce Research Center of Hubei Province, Central China Normal University, Wuhan 430079, China;
3. School of Information, Renmin University of China, Beijing 100872, China
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摘要 

[目的] 提出一种基于评论产品属性情感倾向评估模型(Review Attribute of Product-Based Emotion Evaluate,RAPBEE 模型),用于在线商品虚假评论的识别。[方法] 针对在线商品虚假评论采用评论产品属性情感倾向离群度量方法,结合已有评论效用研究对评论结果进行综合排序,从而得出评论的可信度序列。[结果] 基于R 语言实现,在模型试验集上,通过RAPBEE 模型识别处理后的评论序列和当前商品真实情况的符合度为86.2%,实验结果表明RAPBEE 模型有较强的实际应用能力与适应度。[局限] 需要依赖于已有属性词典的建模方式,在大规模的数据运行效率上有待改进。[结论] 提供一种新的针对中文商品虚假评论识别处理方法,具有较强的扩展能力。

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李志宇
陈燕方
关键词 情感倾向虚假评论垃圾评论商品评论虚假评论识别    
Abstract

[Objective] A model of Review Attribute of Product-Based Emotion Evaluate(RAPBEE) Model is proposed to detect fake reviews of online products. [Methods] Combined with the known research on the reviews effectiveness evaluation, the measuring method of review attribute of product-based emotion outlier detection is used to comprehensive sort the reliability of the reviews, so as to detect the fake reviews. [Results] The test data set is based on the R language to run the model, the results show that after calculated by the RAPBEE model the review sequencing has achieved 86.2% of agreement compared with the real situation which indicates that the RAPBEE model has a strong practical ability and fitness. [Limitations] The model stability depends on the modeling way of the attribute dictionary and the method also can be improved when dealing with large amounts of data set(Big Data). [Conclusions] The paper proposes a new method to deal with the Chinese fake reviews detection of online products, and this method has a strong expandability in reality.

Key wordsEmotion tendency    Fake reviews    Spam reviews    Reviews of online products    Reviews spam detection
收稿日期: 2014-03-24     
:  TP391  
基金资助:

本文系国家大学生创新性实验计划(A 类) 基金项目“在线商品虚假评论识别及其治理研究”(项目编号:220-20111201316)的研究成果之一。

通讯作者: 陈燕方 E-mail:aakas888@icloud.com     E-mail: aakas888@icloud.com
作者简介: 作者贡献声明:陈燕方:研究方案初步设计,问卷设计,论文起草撰写,论文最终版修订;李志宇:研究方案修改,数据采集,清洗,算法设计与实现。
引用本文:   
陈燕方, 李志宇. 基于评论产品属性情感倾向评估的虚假评论识别研究[J]. 现代图书情报技术, 2014, 30(9): 81-90.
Chen Yanfang, Li Zhiyu. Research on Product Review Attribute-Based of Emotion Evaluate Review Spam Detection. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2014.09.11.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.09.11

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