融合特征情感和产品参数的客户感知偏好模型

doi:10.16381/j.cnki.issn1003-207x.2020.09.020

中国管理科学 ›› 2020, Vol. 28 ›› Issue (9): 199-208.doi: 10.16381/j.cnki.issn1003-207x.2020.09.020

融合特征情感和产品参数的客户感知偏好模型

王安宁, 张强, 彭张林, 陆效农, 方钊

1. 合肥工业大学管理学院, 安徽合肥 230009;
2. 过程优化与智能决策教育部重点实验室, 安徽合肥 23009

收稿日期:2018-01-12 修回日期:2018-06-11 出版日期:2020-09-20 发布日期:2020-09-25
通讯作者: 张强(1984-),男(汉族),四川人,合肥工业大学管理学院,副教授,研究方向:产品设计创新、数据分析与信息管理,E-mail:qiang_zhang@hfut.edu.cn. E-mail:qiang_zhang@hfut.edu.cn
基金资助:
国家自然科学基金资助项目（71690235，71501055，71601066）

Customer Preference Model Considering Feature Sentiment and Product Parameters

WANG An-ning, ZHANG Qiang, PENG Zhang-lin, LU Xiao-nong, FANG Zhao

1. School of Management, Hefei University of Technology, Hefei 230009, China;
2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China

Received:2018-01-12 Revised:2018-06-11 Online:2020-09-20 Published:2020-09-25

摘要/Abstract

摘要： 在线评论作为一种公开、可获取的信息资源，蕴含了关于产品质量的各种有价值的信息，对这些信息的分析与挖掘有助于企业了解客户的需求和偏好。目前，基于在线评论的客户偏好模型考虑了特征观点和评论数量，忽视了产品参数对消费者购买决策的影响。因此，本文提出了一种融合特征情感和产品参数的客户感知偏好模型。首先，利用在线评论提取客户讨论的产品特征，并识别产品特征的情感极性，从而进一步计算特征正负面情感得分，生成产品的特征情感。然后，结合企业定义的产品参数，构建特征情感和产品参数融合作用对产品销量影响的计量经济模型，分析客户对产品的感知偏好。最后，为验证模型的有效性，获取了汽车之家网站的39款汽车产品（SUV车型）的评论文本，并持续8个月的销量跟踪。研究结果显示本文提出的模型比信息增益和TF-IDF方法能够更加准确地获取客户的感知偏好。此外，研究结果解释了特征情感和产品参数与产品销量的关联关系，为企业的市场营销和产品设计决策提供了理论基础。

关键词: 在线评论, 客户偏好, 特征情感, 产品参数

Abstract: Online review, which is access to the public, contains a variety of valuable information about product quality. Analyzing and mining of these information is very helpful for the enterprises to understand customer needs and preferences. Currently, the customer preference model based on online reviews considers the number and sentiment of the reviews, but ignores the impact of product parameters on consumers' purchasing decisions. Therefore, a customer preferences model that fuses feature sentiment and product parameters is proposed. First, the product features discussed in the online reviews must be extracted, and the sentiment polarities of the product features should be identified, thereby generating the feature sentiment by measuring the score of positive and negative sentiment. Then, an econometric model combining product parameters provided by the enterprises is built to reflect the influence of the feature sentiment and product parameters on product sales. Finally, in order to verify the effectiveness of our model, an online review text of 39 automotive products (SUV models) from autohome.com has been obtained, and sales tracking has been kept for 8 months. The results show that our model has better performance than the methods of information gain and TF-IDF. It is found that most product features and some product parameters have significant impacts on product sales. Besides, product parameters can enhance customers' perceptions on product quality. In addition, our conclusions explain the impacts of feature sentiment and product parameters on product sales, which provide a theoretical basis for marketing strategy and making product design decisions.

Key words: online reviews, customer preferences, feature sentiment, product parameters

中图分类号:

C931.6

王安宁, 张强, 彭张林, 陆效农, 方钊. 融合特征情感和产品参数的客户感知偏好模型[J]. 中国管理科学, 2020, 28(9): 199-208.

WANG An-ning, ZHANG Qiang, PENG Zhang-lin, LU Xiao-nong, FANG Zhao. Customer Preference Model Considering Feature Sentiment and Product Parameters[J]. Chinese Journal of Management Science, 2020, 28(9): 199-208.

参考文献

[1] Zhou F, Jiao R J, Linsey J S. Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews[J]. Journal of Mechanical Design, 2015, 137(7):071401.
[2] Selden L, MacMillan I C. Manage customer-centric innovation-systematically[J]. Harvard Business Review, 2006, 84(4):108-116.
[3] Feit E M, Beltramo M A, Feinberg F M. Reality check:combining choice experiments with market data to estimate the importance of product attributes[J]. Management Science, 2010, 56(5):785-800.
[4] Tirunillai S, Tellis G J. Mining marketing meaning from online chatter:strategic brand analysis of big data using latent dirichlet allocation[J]. Journal of Marketing Research, 2014, 51(4):463-479.
[5] Tuarob S, Tucker C S. Quantifying product favorability and extracting notable product features using large scale social media data[J]. Journal of Computing and Information Science in Engineering, 2015, 15(3):031003.
[6] Archak N, Ghose A, Ipeirotis P G. Deriving the pricing power of product features by mining consumer reviews[J]. Management Science, 2011, 57(8):1485-1509.
[7] Li Y M, Chen H M, Liou J H, et al. Creating social intelligence for product portfolio design[J]. Decision Support Systems, 2016, 66:123-134.
[8] Xiao S, Wei C P, Dong M. Crowd intelligence:Analyzing online product reviews for preference measurement[J]. Information & Management, 2016, 53(2):169-182.
[9] Lee T Y, BradLow E T. Automated marketing research using online customer reviews[J]. Journal of Marketing Research, 2011, 48(5):881-894.
[10] Decker R, Trusov M. Estimating aggregate consumer preferences from online product reviews[J]. International Journal of Research in Marketing, 2010, 27(4):293-307.
[11] Lee A J T, Yang F C, Chen C H, et al. Mining perceptual maps from consumer reviews[J]. Decision Support Systems, 2016, 82:12-25.
[12] Qi J, Zhang Z, Jeon S, et al. Mining customer requirements from online reviews:A product improvement perspective[J]. Information & Management,2016,53(8):951-963.
[13] Zhang K Z K, Benyoucef M. Consumer behavior in social commerce:A literature review[J]. Decision Support Systems, 2016, 86:95-108.
[14] Tuarob S, Tucker C S. Automated discovery of lead users and latent product features by mining large scale social media networks[J]. Journal of Mechanical Design, 2015, 137(7):071402.
[15] Eirinaki M, Pisal S, Singh J. Feature-based opinion mining and ranking[J]. Journal of Computer and System Sciences, 2012, 78(4):1175-1184.
[16] Halme M, Kallio M. Estimation methods for choice-based conjoint analysis of consumer preferences[J]. European Journal of Operational Research, 2011, 214(1):160-167.
[17] Godes D, Mayzlin D. Using online conversations to study word-of-mouth communication[J]. Marketing Science. 2004, 23(4):545-560.
[18] Chen Y, Xie J. Online consumer review:word-of-mouth as a new element of marketing communication mix[J]. Management Science, 2008, 54(3):477-491.
[19] Sun M. How does the variance of product ratings matter?[J]. Management Science, 2012, 58(4):696-707.
[20] Yoo C W, Sanders G L, Moon J. Exploring the effect of e-WOM participation on e-Loyalty in e-commerce[J]. Decision Support Systems, 2013, 55(3):669-678.
[21] Zhang K Z K, Zhao S J, Cheung C M K, et al. Examining the influence of online reviews on consumers' decision-making:A heuristic-systematic model[J]. Decision Support Systems, 2014, 67:78-89.
[22] 冯娇,姚忠. 基于社会学习理论的在线评论信息对购买决策的影响研究[J]. 中国管理科学, 2016, 24(09):106-114.
[23] 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.
[24] Forman C, Ghose A, Wiesenfeld B. Examining the relationship between reviews and sales:the role of reviewer identity disclosure in electronic markets[J]. Information Systems Research, 2008, 19(3):291-313,393-395.
[25] Zhu F, Zhang X. Impact of online consumer reviews on sales:the moderating role of product and consumer characteristics[J]. Journal of Marketing, 2010, 74(2):133-148.
[26] Asur S, Huberman B A. Predicting the future with social media[C]//IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 492-499, 2010.
[27] 李实,叶强,李一军,Rob Law. 中文网络客户评论的产品特征挖掘方法研究[J]. 管理科学学报, 2009, 12(2):142-152.
[28] 王伟, 王洪伟. 特征观点对购买意愿的影响:在线评论的情感分析方法[J]. 系统工程理论与实践, 2016, 36(1):63-76.
[29] Hu M, Liu B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, August 22-25, ACM Press 2004:168-177.
[30] Hu M, Liu B. Mining opinion features in customer reviews[C]//Proceedings of the 19th National Conference on Artifical Intelligence, San Jose, USA, July, AAAI Press, 2004:755-760.
[31] Jin J, Ji P, Kwong C K. What makes consumers unsatisfied with your products:Review analysis at a fine-grained level[J]. Engineering Applications of Artificial Intelligence, 2016, 47:38-48.
[32] Tucker C S, Kim H M. Trend mining for predictive product design[J]. Journal of Mechanical Design, 2011, 133(11):111008.

融合特征情感和产品参数的客户感知偏好模型

Customer Preference Model Considering Feature Sentiment and Product Parameters

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