Please wait a minute...
Advanced Search
现代图书情报技术  2014, Vol. 30 Issue (5): 58-65    DOI: 10.11925/infotech.1003-3513.2014.05.08
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
卖家描述与买家评论相符度模型研究
王倩倩, 袁勤俭
南京大学信息管理学院 南京 210093
The Research of Conformity Model Between Sellers Description and Buyers Comments
Wang Qianqian, Yuan Qinjian
School of Information Management, Nanjing University, Nanjing 210093, China
全文: PDF(1328 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】通过构建买家评论与卖家描述的相符度模型, 对淘宝中卖家描述的商品特性与买家评论是否一致进行探讨。【方法】研究卖家的商品描述和买家的评论这两个文本信息, 提取产品属性特征词和判断情感词极性, 最后选取三家淘宝网店进行模型评估实验。【结果】发现B商家宝贝描述与买家评论相符度较高, A店次之, C店最差。其中, C店的“里衬”和“正品”两个产品属性, 卖家描述与买家评论不相符。【局限】卖家描述的内容和买家评论的内容未能全面涉及, 没有包括卖家的商品标题信息、卖家的图片说明信息以及买家秀中买家提供的照片信息。【结论】经过模型计算后的结果能够更细节、准确地反映出商品在哪些属性上相符以及多大程度上相符, 进而更有效地辅助消费者进行决策。

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
王倩倩
袁勤俭
关键词 文本挖掘属性特征词情感分析相符度计算    
Abstract

[Objective] This study discusses wheather commodity characteristics described by sellers are consistent with comments or not, by building the conformity model between description of sellers and comments of buyers. [Methods] Study the text of description and comments, extract the key attributes of products and determine polarity of emotional words, then select three Taobao shops to evaluate the model. [Results] The result shows that there are higher consistent degrees in B shop, A shop is the second, C shop is the worst. There are two attributes “in line” and “authentic” in C shop, which are not consistent with the comments. [Limitations] All the information from sellers and customers are not contained, such as title and picture information of products, and the photo information from customers. [Conclutions] The results can tell which attributes are consistent with the sellers description and how much they match. This result can support consumer’s decisions more effectively.

Key wordsText mining    Key properties    Emotional analysis    Consistent degree calculation
收稿日期: 2014-01-03     
:  F224  
通讯作者: 王倩倩 E-mail:252884250@qq.com   
作者简介: 王倩倩: 提出命题、设计方法和模型, 数据获取与分析, 论文撰写; 袁勤俭: 指导修改论文, 以及最终版本修订。
引用本文:   
王倩倩, 袁勤俭. 卖家描述与买家评论相符度模型研究[J]. 现代图书情报技术, 2014, 30(5): 58-65.
Wang Qianqian, Yuan Qinjian. The Research of Conformity Model Between Sellers Description and Buyers Comments. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2014.05.08.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.05.08

[1] 张红斌, 李广丽. 商品在线评价的情感倾向性分析研究[J]. 现代图书情报技术, 2012(10): 61-66. (Zhang Hongbin, Li Guangli. Research on Sentiment Orientation Analysis of Product Online Reviews[J]. New Technology of Library and Information Service, 2012(10):61-66.)
[2] 杨铭, 祁巍, 闫相斌, 等.在线商品评论的效用分析研究[J].管理科学学报, 2012, 15(5): 65-75. (Yang Ming, Qi Wei, Yan Xiangbin, et al. Utility Analysis for Online Product Review [J]. Journal of Management Sciences in China, 2012, 15(5): 65-75.)
[3] 郝媛媛, 叶强, 李一军. 基于影评数据的在线评论有用性影响因素研究[J]. 管理科学学报, 2010, 13(8):78-88.( 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.)
[4] Chen C C, Tseng Y.Quality Evaluation of Product Reviews Using an Information Quality Framework[J]. Decision Support Systems, 2011, 50(4): 755-768.
[5] Chklovski T. Deriving Quantitative Overviews of Free Text Assessments on the Web[C]. In: Proceedings of the 11th International Conference on Intelligent User Interfaces (IUI'06). New York: ACM, 2006: 155-162.
[6] 李志宇. 在线商品评论效用排序模型研究[J]. 现代图书情报技术, 2013(4): 62-68. (Li Zhiyu. Study on the Reviews Effectiveness Sequencing Model of Online Products[J]. New Technology of Library and Information Service, 2013 (4): 62-68.)
[7] Kobayashi N, Inui K, Matsumoto Y, et al. Collecting Evaluative Expressions for Opinion Extraction[C]. In: Procee-dings of the 1st International Joint Conference on Natural Language Processing (IJCNLP'04). Berlin, Heidelberg: Springer-Verlag, 2004: 596-605.
[8] Popescu A, Etzioni O. Extracting Product Features and Opinions from Reviews[C]. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT'05). Stroudsburg: Association for Computational Linguistics, 2005: 339-346.
[9] Miao Q, Li Q, Dai R. Amazing: A Sentiment Mining and Retrieval System[J]. Expert Systems with Applications, 2009, 36(3): 7192-7198.
[10] 伍星, 何中市, 黄永文. 产品评论挖掘研究综述[J]. 计算机工程与应用, 2008, 44(36): 37-41. (Wu Xing, He Zhongshi, Huang Yongwen. Product Review Mining: A Survey[J]. Computer Engineering and Applications, 2008, 44(36): 37-41.)
[11] Liu B, Hu M, Cheng J. Opinion Observer: Analyzing and Comparing Opinions on the Web [C]. In: Proceedings of the 14th International World Wide Web Conference (WWW'05). New York: ACM, 2005: 342-351.
[12] Pang B, Lee L. Opinion Mining and Sentiment Analysis[J]. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.
[13] 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.)
[14] GooSeeker. 网页抓取软件MetaSeeker[EB/OL]. [2013-03-05]. http://www.gooseeker.com/cn/node/product/metaseeker_ intro. (GooSeeker. Web Capture Software MetaSeeker [EB/ OL]. [2013-03-05]. http://www.gooseeker.com/cn/node/product/ metaseeker_intro.)
[15] 刘群, 张华平, 俞鸿魁, 等. 基于层叠隐马模型的汉语词法分析[J]. 计算机研究与发展, 2004, 41(8): 1421-1429.(Liu Qun, Zhang Huaping, Yu Hongkui, et al. Chinese Lexical Analysis Using Cascaded Hidden Markov Model[J]. Journal of Computer Research and Development, 2004, 41(8): 1421-1429.)
[16] 数据堂. 台湾大学NTUSD -简体中文情感极性词典[EB/OL]. [2013-03-05]. http://www.datatang.com/data/11837. (Data Tang. National Taiwan University-The Polarity of Simplified Chinese Emotional Dictionary [EB/OL]. [2013- 03-05]. http://www.datatang.com/data/11837.)
[17] Ye Q, Zhang Z, Law R.Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches[J]. Expert Systems with Applications, 2009, 36(3): 6527-6535.

[1] 尤众喜,华薇娜,潘雪莲. 中文分词器对图书评论和情感词典匹配程度的影响 *[J]. 数据分析与知识发现, 2019, 3(7): 23-33.
[2] 杨亚楠,赵文辉,张健,谭珅,张贝贝. 基于多视图协同的政策文本可视化研究*[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[3] 张梦吉,杜婉钰,郑楠. 引入新闻短文本的个股走势预测模型[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[4] 蒋翠清,郭轶博,刘尧. 基于中文社交媒体文本的领域情感词典构建方法研究*[J]. 数据分析与知识发现, 2019, 3(2): 98-107.
[5] 余本功,张培行,许庆堂. 基于F-BiGRU情感分析的产品选择方法*[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[6] 曾子明,杨倩雯. 基于LDA和AdaBoost多特征组合的微博情感分析*[J]. 数据分析与知识发现, 2018, 2(8): 51-59.
[7] 张宁,尹乐民,何立峰. 网络股评“发布者-关注者”BSI与股票市场关联性研究*[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[8] 王秀芳,盛姝,路燕. 一种基于话题聚类及情感强度的微博舆情分析模型*[J]. 数据分析与知识发现, 2018, 2(6): 37-47.
[9] 杨斯楠,徐健,叶萍萍. 网络评论情感可视化技术方法及工具研究*[J]. 数据分析与知识发现, 2018, 2(5): 77-87.
[10] 王婷婷,王凯平,戚桂杰. 基于情感分析的开放式创新平台创意采纳研究: 以Salesforce为例*[J]. 数据分析与知识发现, 2018, 2(4): 38-47.
[11] 范馨月,崔雷. 基于文本挖掘的药物副作用知识发现研究[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[12] 赵杨,李齐齐,陈雨涵,曹文航. 基于在线评论情感分析的海淘APP用户满意度研究*[J]. 数据分析与知识发现, 2018, 2(11): 19-27.
[13] 何跃,朱灿. 基于微博的意见领袖网情感特征分析*——以“非法疫苗”事件为例[J]. 数据分析与知识发现, 2017, 1(9): 65-73.
[14] 张红丽,刘济郢,杨斯楠,徐健. 基于网络用户评论的评分预测模型研究*[J]. 数据分析与知识发现, 2017, 1(8): 48-58.
[15] 高歌,罗珺玫,王宇. 基于HNC理论的文本情感倾向性分析*[J]. 数据分析与知识发现, 2017, 1(8): 85-91.
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