Please wait a minute...
Advanced Search
现代图书情报技术  2013, Vol. 29 Issue (9): 60-66    DOI: 10.11925/infotech.1003-3513.2013.09.10
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
在线中文商品评论可信度研究
孟美任, 丁晟春
南京理工大学信息管理系 南京 210094
Research on the Credibility of Online Chinese Product Reviews
Meng Meiren, Ding Shengchun
Department of Information Management, Nanjing University of Science & Technology, Nanjing 210094, China
全文: PDF(493 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 对在线中文商品评论中可信度较低的评论信息进行过滤,为消费者提供对制定购买决策有帮助的评论。在深入分析在线中文商品评论特点的基础上,结合相关研究成果,通过问卷调查进行可信度影响因素的实证分析。根据实证结果,选取内容完整性、情感平衡性、评论时效性以及发布者身份明确性4类特征,采用CRFs模型进行评论可信度4级分类,并进行特征组合实验,得到最佳特征组合。实验效果显著,分类模型正确率均在75%以上。该研究成果可以用于改善现有的“人工效用评价”方式,为在线评论的优化过滤提供一种新的方法与思路。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
孟美任
丁晟春
关键词 在线商品评论可信度CRFs模型影响因素效用评价    
Abstract:This paper aims at filtering the lower credible online Chinese product reviews to offer valuable reviews for consumers’ purchase decision. Based on the deep analysis of the online Chinese product reviews’ characteristics, also with some related works, the authors make an empirical analysis on the credibility factors through questionnaires. According to the results of the empirical analysis, the authors select content integrity, emotional balance, review timeliness and clarity of the identity of the publisher as four features, use CRFs as reviews credibility’s classification model, and conduct feature combination experiments to get the best feature combination. The experiments achieve significant results, and the correct rates of the classification model are all above 75%. The research results of this paper can improve the existing artificial effectiveness evaluation method, thus offering new methods and thoughts for optimized filtering of the online reviews.
Key wordsOnline product reviews    Credibility    CRFs model    Affecting factor    Effectiveness evaluation
收稿日期: 2013-06-19     
:  G353.1  
基金资助:本文系国家自然科学基金项目“基于文本语义挖掘的商品评论信息可信度分析研究”(项目编号:71103085)的研究成果之一。
通讯作者: 孟美任     E-mail: memory8.8mmr@gmail.com
引用本文:   
孟美任, 丁晟春. 在线中文商品评论可信度研究[J]. 现代图书情报技术, 2013, 29(9): 60-66.
Meng Meiren, Ding Shengchun. Research on the Credibility of Online Chinese Product Reviews. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2013.09.10.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2013.09.10
[1] 陈江涛, 张金隆, 张亚军. 在线商品评论有用性影响因素研究:基于文本语义视角[J]. 图书情报工作, 2012, 56(10):119-123. (Chen Jiangtao, Zhang Jinlong, Zhang Yajun. Impact Factors of Online Customer Reviews Usefulness: A Text Semantics Approach [J]. Library and Information Service, 2012, 56(10):119-123.)
[2] 郝媛媛, 叶强, 李一军. 基于影评数据的评论有用性影响因素研究[J]. 管理科学学报, 2010, 13(8):78-88,96. (Hao Yuanyuan, Ye Qiang, Li Yijun. Research on Online Impact Factors of Customer Reviews Usefulness Based on Movie Reviews Data [J]. Journal of Management Sciences in China, 2010, 13(8):78-88,96.)
[3] Otterbacher J. "Helpfulness" in Online Communities: A Measure of Message Quality[C]. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2009:955-964.
[4] 李念武, 岳蓉. 网络口碑可信度及其对购买行为之影响的实证研究[J]. 图书情报工作, 2009, 53(22):133-137.(Li Nianwu, Yue Rong. An Empirical Study on Credibility of Online Word-of-Mouth and Its Effects on Consumers’ Purchase Behavior [J]. Library and Information Service, 2009, 53(22):133-137.)
[5] Liu Y, Huang X J, An A, et al. Modeling and Predicting the Helpfulness of Online Reviews[C]. In: Proceedings of the 8th IEEE International Conference on Data Mining. Washington: IEEE Computer Society, 2008:443-452.
[6] Ghose A, Ipeirotis P G. Designing Novel Review Ranking Systems: Predicting the Usefulness and Impact of Reviews[C]. In: Proceedings of the 9th International Conference on Electronic Commerce. New York, NY, USA: ACM, 2007:303-310.
[7] Kim S M, Pantel P, Chklovski T, et al. Automatically Assessing Review Helpfulness[C]. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics,2006:423-430.
[8] Weimer M, Gurevych I. Predicting the Perceived Quality of Web Forum Posts[C]. In: Proceedings of Recent Advances in Natural Language Processing, Bororets, Bulgaria. 2007:643-648.
[9] Zhang Z. Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications [J]. IEEE Intelligent Systems, 2008, 23(5):42-49.
[10] Abbasi A, Chen H, Salem A. Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums [J]. ACM Transactions on Information Systems, 2008, 26(3): 1-34.
[11] Ahmed A, Hsinchun C. Applying Authorship Analysis to Extremist-Group Web Forum Messages [J]. IEEE Intelligent Systems, 2005, 20(5):67-75.
[12] Hu N, Liu L, Sambamurthy V. Fraud Detection in Online Consumer Reviews[J]. Decision Support Systems, 2011, 50(3):614-626.
[13] Hu N, Bose I, Gao Y J, et al. Manipulation in Digital Word of Mouth: A Reality Check for Book Reviews [J]. Decision Support Systems, 2011, 50(3):627-635.
[14] Liu B. Web Data Mining [M]. Berlin: Springer, 2009:316-317.
[15] Jindal N, Liu B. Review Spam Detection[C]. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada. New York,NY,USA: ACM, 2007:1189-1190.
[16] Wu G, Greene D, Smyth B, et al. Distortion as a Validation Criterion in the Identification of Suspicious Reviews[C]. In: Proceedings of the 1st Workshop on Social Media Analytics. Washington,DC,USA: ACM, 2010:10-13.
[17] 李霄, 丁晟春. 垃圾商品评论信息的识别研究[J]. 现代图书情报技术, 2013(1):63-68. (Li Xiao, Ding Shengchun. Research on Review Spam Recognition [J]. New Technology of Library and Information Service, 2013(1):63-68.)
[18] Lafferty J D, McCallum A, Pereira F C N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data[C].In: Proceedings of the 18th International Conference on Machine Learning. San Francisco,CA,USA: Morgan Kaufmann Publishers Inc., 2001:282-289.
[19] 叶强, 张紫琼, 罗振雄. 面向互联网评论情感分析的中文主观性自动判别方法研究[J]. 信息系统学报, 2007, 1(1):79-91. (Ye Qiang, Zhang Ziqiong, Luo Zhenxiong. Automatically Measuring Subjectivity of Chinese Sentences for Sentiment Analysis to Reviews of the Internet[J]. China Journal of Information Systems, 2007, 1(1):79-91.)
[1] 王玲,代前进,吴晓隽. 基于预警平台大数据的事件旅游客流时空分布研究*[J]. 数据分析与知识发现, 2018, 2(8): 31-40.
[2] 王婷婷,王宇,秦琳杰. 基于动态主题模型的时间窗口划分研究*[J]. 数据分析与知识发现, 2018, 2(10): 54-64.
[3] 李保珍,王亚,周可. 基于贝叶斯理论的社会化媒体网络信息内容可信度测度*[J]. 数据分析与知识发现, 2017, 1(6): 83-92.
[4] 王晓玉,李斌. 基于CRFs和词典信息的中古汉语自动分词*[J]. 数据分析与知识发现, 2017, 1(5): 62-70.
[5] 王忠群,吴东胜,蒋胜,皇苏斌. 一种基于主流特征观点对的评论可信性排序研究*[J]. 数据分析与知识发现, 2017, 1(10): 32-42.
[6] 肖学斌,柴艳菊. 论文的相关参数与被引频次的关系研究[J]. 现代图书情报技术, 2016, 32(6): 46-53.
[7] 罗政,李玉纳. 企业价值链协同知识创新影响因素的系统动力学建模与仿真[J]. 现代图书情报技术, 2016, 32(5): 80-90.
[8] 廖海涵, 王曰芬. 社交媒体舆情信息传播效果影响因素研究*——以新浪微博“8.12天津爆炸”事件为例[J]. 数据分析与知识发现, 2016, 32(12): 85-93.
[9] 郝玫, 杨晓媛. 中文网络客户评论可信度研究[J]. 现代图书情报技术, 2015, 31(2): 55-63.
[10] 聂卉, 王佳佳. 产品评论垃圾识别研究综述[J]. 现代图书情报技术, 2014, 30(2): 63-71.
[11] 周沛, 马静, 徐晓林. 企业移动电子税务采纳影响因素的实证研究[J]. 现代图书情报技术, 2012, 28(3): 59-66.
[12] 彭希羡, 冯祝斌, 孙霄凌, 朱庆华. 微博用户持续使用意向的理论模型及实证研究[J]. 现代图书情报技术, 2012, (11): 78-85.
[13] 万君, 张祥, 庞培培. 婚恋网站初始信任影响因素模型研究[J]. 现代图书情报技术, 2012, (10): 67-71.
[14] 苏金燕. 我国网络学术信息空间分布影响因素研究——基于空间计量的实证分析[J]. 现代图书情报技术, 2011, 27(5): 62-68.
[15] 甘利人,许应楠. 企业信息系统用户接受行为影响因素研究——以ERP系统为例[J]. 现代图书情报技术, 2009, 3(2): 71-77.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn