基于在线评论数据挖掘和Kano模型的产品需求分析

管理评论 ›› 2022, Vol. 34 ›› Issue (11): 109-117.

基于在线评论数据挖掘和Kano模型的产品需求分析

张振刚^1,2, 罗泰晔³

1. 华南理工大学工商管理学院, 广州 510640;
2. 广州数字创新研究中心, 广州 510640;
3. 仲恺农业工程学院管理学院, 广州 510225

收稿日期:2020-05-06 出版日期:2022-11-28 发布日期:2022-12-30
通讯作者: 罗泰晔(通讯作者),仲恺农业工程学院管理学院讲师,博士。
作者简介:张振刚,华南理工大学工商管理学院教授,广州数字创新研究中心主任,博士生导师,博士。
基金资助:
国家社会科学基金重大项目(18ZDA062)。

Product Demand Analysis Based on Online Review Data Mining and Kano Model

Zhang Zhengang^1,2, Luo Taiye³

1. School of Business Administration, South China University of Technology, Guangzhou 510640;
2. Guangzhou Digital Innovation Research Center, Guangzhou 510640;
3. School of Management, Zhongkai University of Agriculture and Engineering, Guangzhou 510225

Received:2020-05-06 Online:2022-11-28 Published:2022-12-30

摘要/Abstract

摘要： 本文在Kano模型的基础上,提出一种基于在线评论数据挖掘来识别产品需求类型的方法。从在线评论中提取产品属性并进行情感分析,在此基础上构建识别不同属性需求的分类模型。本文以4款5G手机的在线评论数据为素材进行实例分析,将消费者对5G手机产品属性的需求划分为期望需求、必备需求、魅力需求和无差异需求四类,并以满意度和关注度为指标对5G手机产品属性的重要程度和需改进程度进行了排序。

关键词: 在线评论, Kano模型, 产品需求, 情感分析

Abstract: Based on Kano model and online review data mining, this paper proposes a method to identify product demand types. Product attributes are extracted from online reviews and sentiment analysis is carried out. Then a classification model for identifying different attribute demands is proposed. Taking the online review data of four 5G mobile phones as an example, this paper analyzes consumers’ attribute demands of 5G mobile phone products, which are divided into four types: one-dimension demand, must-be demand, attractive demand and indifferent demand. The importance degree and the to-be-improved degree of the attributes of 5G mobile phones are ranked by satisfaction degree and attention degree.

Key words: online review, Kano model, product demand, emotional analysis

张振刚, 罗泰晔. 基于在线评论数据挖掘和Kano模型的产品需求分析[J]. 管理评论, 2022, 34(11): 109-117.

Zhang Zhengang, Luo Taiye. Product Demand Analysis Based on Online Review Data Mining and Kano Model[J]. Management Review, 2022, 34(11): 109-117.

参考文献

[1] 赵宇晴,阮平南,刘晓燕,等. 基于在线评论的用户满意度评价研究[J]. 管理评论, 2020,32(3):179-189
[2] 朱晓霞,宋嘉欣,张晓缇. 基于主题挖掘技术的文本情感分析综述[J]. 情报理论与实践, 2019,42(11):156-163
[3] 唐晓波,兰玉婷. 基于特征本体的微博产品评论情感分析[J]. 图书情报工作, 2016,60(16):121-127
[4] 何跃,赵书朋,何黎. 基于情感知识和机器学习算法的组合微文情感倾向分类研究[J]. 情报杂志, 2018,37(5):189-194
[5] Awajan A. Keyword Extraction from Arabic Documents Using Term Equivalence Classes[J]. ACM Transactions on Asian and LowResource Language Information Processing, 2015,14(2):1-18
[6] 聂卉. 隐主题模型下产品评论观点的凝聚与量化[J]. 情报学报, 2017,36(6):565-573
[7] 睢国钦,那日萨,彭振. 基于深度学习和CRFs的产品评论观点抽取方法[J]. 情报杂志, 2019,38(5):177-185
[8] 郗亚辉. 产品评论特征及观点抽取研究[J]. 情报学报, 2014,33(3):326-336
[9] 郭红梅,张智雄. 基于图挖掘的文本主题识别方法研究综述[J]. 中国图书馆学报, 2015,41(6):97-108
[10] Wang Z. B., Wan M. Y., Cui X. H., et al. Personalized Recommendation Algorithm Based on Product Reviews[J]. Journal of Electronic Commerce in Organizations, 2018,16(3):22-38
[11] Wang W. X., Feng Y., Dai W. Q. Topic Analysis of Online Reviews for Two Competitive Products Using Latent Dirichlet Allocation[J]. Electronic Commerce Research and Applications, 2018,29:142-156
[12] Fan Z., Che Y., Chen Z. Product Sales Forecasting Using Online Reviews and Historical Sales Data:A Method Combining the Bass Model and Sentiment Analysis[J]. Journal of Business Research, 2017,74:90-100
[13] Xiao S., Wei C. P., Dong M. Crowd Intelligence:Analyzing Online Product Reviews for Preference Measurement[J]. Information & Management, 2016,53(2):169-182
[14] Chen K., Kou G., Shang J., et al. Visualizing Market Structure through Online Product Reviews:Integrate Topic Modeling, TOPSIS, and Multi-dimensional Scaling Approaches[J]. Electronic Commerce Research and Applications, 2015,14:58-74
[15] Jeong B., Yoon J., Lee J. Social Media Mining for Product Planning:A Product Opportunity Mining Approach Based on Topic Modeling and Sentiment Analysis[J]. International Journal of Information Management, 2019,48:280-290
[16] Liang S. W., Lu H. P., Kuo T. K. A Study on Using the Kano Two-Dimensional Quality Model to Evaluate the Service Quality of Government Websites[J]. Journal of Internet Technology, 2014,15(2):149-162
[17] Wu H. H., Shieh J., Lu Y. M. An Integrated Framework of Refined Kano's and Kano-CKM Models in Classifying Customer Requirements[J]. Journal of Statistics & Management Systems, 2013,16(6):349-362
[18] 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
[19] 李梦楠,周秀会. 基于Kano模型的高校图书馆微信公众号服务内容分类和供给优先序研究[J]. 图书情报工作, 2019,63(10):39-47
[20] 于唤洲,司晨曦,王慧文. 基于模糊KANO模型的产品迭代和服务创新研究[J]. 科技管理研究, 2018,38(9):24-31
[21] 吴江,周露莎,刘冠君,等. 基于LDA的可穿戴设备在线评论主题挖掘研究[J]. 信息资源管理学报, 2017,7(3):24-33
[22] Guo Y., Barnes S. J., Jia Q. Mining Meaning from Online Ratings and Reviews:Tourist Satisfaction Analysis Using Latent Dirichlet Allocation[J]. Tourism Management, 2017,59(4):467-483
[23] Bi J. W., Liu Y., Fan Z. P., et al. Modelling Customer Satisfaction from Online Reviews Using Ensemble Neural Network and Effect-Based Kano Model[J]. International Journal of Production Research, 2019,57(22):7068-7088
[24] Xiao S., Wei C. P., Dong M. Crowd Intelligence:Analyzing Online Product Reviews for Preference Measurement[J]. Information & Management, 2016,53(2):169-182
[25] 徐琳宏,林鸿飞,潘宇,等. 情感词汇本体的构造[J]. 情报学报, 2008,27(2):180-185
[26] 李慧,柴亚青. 基于属性特征的评论文本情感极性量化分析[J]. 数据分析与知识发现, 2017,1(10):1-11
[27] 蔺璜,郭姝慧. 程度副词的特点范围与分类[J]. 山西大学学报(哲学社会科学版), 2003,(2):71-74
[28] 唐中君,龙玉玲. 基于Kano模型的个性化需求获取方法研究[J]. 软科学, 2012,26(2):127-131