Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (9): 17-25    DOI: 10.11925/infotech.1003-3513.2015.09.03
Current Issue | Archive | Adv Search |
Research on Metrics-Model for Online Product Review Depth Based on Domain Expert and Feature Concept Tree of Products
Wang Zhongqun, Huang Subin, Xiu Yu, Zhang Yi
School of Management Engineering, Anhui Polytechnic University, Wuhu 241000, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] Solve the problem which only use the length of online product review to measure the review depth. [Methods] In this paper, a metrics-model for online product review depth is proposed. Firstly, on the basis of analyzing the demand information of customers for making decision, the concept of review depth is defined and feature concept tree of product is introduced. Secondly, the metrics-model for measuring product review depth is presented according to the features of the product review from domain experts and the distribution of product features over feature concept tree of product. [Results] Empirical study demonstrates that the metrics-model is identical to the model for review helpfulness, and the result shows that the model is feasible. [Limitations] This paper does not involve the product usage scenario of consumers and the review depth measurement for experience products. [Conclusions] The metrics-model can measure product review depth more accurately.

Received: 03 March 2015      Published: 06 April 2016
:  G202  

Cite this article:

Wang Zhongqun, Huang Subin, Xiu Yu, Zhang Yi. Research on Metrics-Model for Online Product Review Depth Based on Domain Expert and Feature Concept Tree of Products. New Technology of Library and Information Service, 2015, 31(9): 17-25.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.09.03     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I9/17

[1] 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.)
[2] Chevalier J A, Mayzlin D. The Effect of Word of Mouth on Sales: Online Book Reviews [J]. Journal of Marketing Research, 2006, 43(3): 345-354.
[3] Dellarocas C, Zhang X Q, Awad N F. Exploring the Value of Online Product Reviews in Forecasting Sales: The Case of Motion Pictures [J]. Journal of Interactive Marketing, 2007, 21(4): 23-45.
[4] 王平, 代宝. 消费者在线评论有用性影响因素实证研究[J].统计与决策, 2012(2): 118-120. (Wang Ping, Dai Bao. An Empirical Study of the Impact Factors of Online Reviews Helpfulness for Consumers [J]. Statistics and Decision, 2012(2): 118-120.)
[5] Mudambi S M, Schuff D. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com [J]. MIS Quarterly, 2010, 34(1): 185-200.
[6] 付建坤, 侯伦, 方佳明. 考虑品牌声誉影响下的在线评论有用性研究[J]. 软科学, 2014, 28(3): 97-100. (Fu Jiankun, Hou Lun, Fang Jiaming. Study on the Helpfulness of Online Reviews by Considering the Influence of Brand Reputation [J]. Soft Science, 2014, 28(3): 97-100.)
[7] 彭岚, 周启海, 邱江涛. 消费者在线评论有用性影响因素模型研究[J]. 计算机科学, 2011, 38(8): 205-207. (Peng Lan, Zhou Qihai, Qiu Jiangtao. Research on the Model of Helpfulness Factors of Online Customer Reviews [J]. Computer Science, 2011, 38(8): 205-207.)
[8] 许应楠, 甘利人. 面向推荐服务的消费者在线商品选择决策中的知识支持分析[J]. 情报理论与实践, 2013, 36(3): 107-111, 116. (Xu Yingnan, Gan Liren. Knowledge Support Analysis in Online Product Selection Decision-Making for Recommendation Service-Oriented Consumers [J]. Information Studies: Theory & Application, 2013, 36(3): 107-111, 116.)
[9] 刘青磊, 顾小丰. 基于《知网》的词语相似度算法研究[J].中文信息学报, 2010, 24(6): 31-36. (Liu Qinglei, Gu Xiaofeng. Study on HowNet-Based Word Similarity Algorithm [J]. Journal of Chinese Information Processing, 2010, 24(6): 31-36.)
[10] 黄果, 周竹荣. 基于领域本体的概念语义相似度计算研究[J]. 计算机工程与设计, 2007, 28(10): 2460-2463. (Huang Guo, Zhou Zhurong. Research on Domain Ontology-Based Concept Semantic Similarity Computation [J]. Computer Engineering and Design, 2007, 28(10): 2460-2463.)
[11] 张映海. 基于概念树扩展的中文文本检索研究[J]. 计算机工程与应用, 2008, 44(26): 154-157. (Zhang Yinghai. Research on Chinese Text Retrieval Based on Expansion of Concept Tree [J]. Computer Engineering and Applications, 2008, 44(26): 154-157.)
[12] 郝玫, 王道平. 面向供应链的产品评论中客户关注特征挖掘方法研究[J]. 现代图书情报技术, 2014(4): 65-70. (Hao Mei, Wang Daoping. Mining Customer Focus Features from Product Reviews Oriented Supply Chain [J]. New Technology of Library and Information Service, 2014(4): 65-70.)
[13] ICTCLAS [EB/OL]. [2014-11-28]. http://ictclas.nlpir.org/.
[14] HIT-CIR Tongyici Cilin (Extended) [EB/OL]. [2014-11-28]. http://ir.hit.edu.cn/demo/ltp/Sharing_Plan.htm.
[15] 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.)

[1] Fan Tao,Wang Hao,Wu Peng. Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
[2] Zhou Zeyu,Wang Hao,Zhao Zibo,Li Yueyan,Zhang Xiaoqin. Construction and Application of GCN Model for Text Classification with Associated Information[J]. 数据分析与知识发现, 2021, 5(9): 31-41.
[3] Feng Yong,Liu Yang,Xu Hongyan,Wang Rongbing,Zhang Yonggang. Recommendation Model Incorporating Neighbor Reviews for GRU Products[J]. 数据分析与知识发现, 2021, 5(3): 78-87.
[4] Wu Jinming,Hou Yuefang,Cui Lei. Automatic Expression of Co-occurrence Clustering Based on Indexing Rules of Medical Subject Headings[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[5] Zhao Yang, Zhang Zhixiong, Liu Huan, Ding Liangping. Classification of Chinese Medical Literature with BERT Model[J]. 数据分析与知识发现, 2020, 4(8): 41-49.
[6] Zhixiong Zhang,Huan Liu,Liangping Ding,Pengmin Wu,Gaihong Yu. Identifying Moves of Research Abstracts with Deep Learning Methods[J]. 数据分析与知识发现, 2019, 3(12): 1-9.
[7] Yan Yu,Lei Chen,Jinde Jiang,Naixuan Zhao. Measuring Patent Similarity with Word Embedding and Statistical Features[J]. 数据分析与知识发现, 2019, 3(9): 53-59.
[8] Xiong Huixiang,Ye Jiaxin,Jiang Wuxuan. Clustering Social Tags with Improved DBSCAN Algorithm[J]. 数据分析与知识发现, 2018, 2(12): 77-88.
[9] He Weilin,Feng Guohe,Xie Hongling. Analyzing Scientific Literature with Content Similarity - Topics over Time Model[J]. 数据分析与知识发现, 2018, 2(11): 64-72.
[10] Yin Cong,Zhang Liyi. Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O[J]. 数据分析与知识发现, 2018, 2(11): 28-36.
[11] Hu Jiaheng,Cen Yonghua,Wu Chengyao. Constructing Sentiment Dictionary with Deep Learning: Case Study of Financial Data[J]. 数据分析与知识发现, 2018, 2(10): 95-102.
[12] Xu Jianmin,Xu Caiyun. Computing Similarity of Sci-Tech Documents Based on Texts and Formulas[J]. 数据分析与知识发现, 2018, 2(10): 103-109.
[13] Zhang Yanfeng,Li He,Peng Lihui,Hou Litie. Identifying Useful Online Reviews with Semantic Feature Extraction[J]. 数据分析与知识发现, 2017, 1(12): 74-83.
[14] Wei Xing,Hu Dehua,Yi Minhan,Zhu Qizhen,Zhu Wenjie. Extracting Disease-Gene-Drug Correlations Based on Data Cube[J]. 数据分析与知识发现, 2017, 1(10): 94-104.
[15] Wang Zhongqun,Wu Dongsheng,Jiang Sheng,Huang Subin. Ranking Credibility of Online Product Reviews Based on Feature-Opinion Pair[J]. 数据分析与知识发现, 2017, 1(10): 32-42.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn