图书情报工作

图书情报工作 >

2025 , Vol. 69 >Issue 14: 73 - 90

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.14.007

研究论文

代际产品视角下用户需求及产品细分特征的动态演化分析——基于在线评论挖掘

  • 李尽法 ,
  • 宋孟亭 ,
  • 张珂 ,
  • 仵轩
展开
  • 1 郑州大学管理学院 郑州 450001;
    2 河南省创新方法工程技术研究中心 郑州 450001;
    3 郑州大学信息管理学院 郑州 450001;
    4 郑州市数据科学研究中心 郑州 450001;
    5 北京大学信息管理系 北京 100871
李尽法，教授，博士；宋孟亭，硕士；张珂，副研究员，博士，通信作者，E-mail:kezhang@zzu.edu.cn；仵轩，博士研究生。

收稿日期: 2024-07-19

  修回日期: 2024-10-15

  网络出版日期: 2025-07-14

基金资助

本文系国家自然科学基金青年基金项目“数据与知识融合驱动的智能产品服务创新需求全景识别及创新方案优化研究”（项目编号：72401262）和中国博士后科学基金第75批面上资助项目“多模态数据驱动的智能产品服务创新需求感知及创新方案优化研究”（项目编号：2024M752968）研究成果之一。

收起

Dynamic Evolution Analysis of User Requirements and Product Segmentation Features from the Perspective of Intergenerational Products: Based on Online Reviews Mining

  • Li Jinfa ,
  • Song Mengting ,
  • Zhang Ke ,
  • Wu Xuan
Expand
  • 1 School of Management, Zhengzhou University, Zhengzhou 450001;
    2 Henan Innovative Methods Engineering Technology Research Center, Zhengzhou 450001;
    3 School of Information Management, Zhengzhou University, Zhengzhou 450001;
    4 Zhengzhou Research Center of Data Science, Zhengzhou 450001;
    5 Department of Information Management, Peking University, Beijing 100871
Li Jinfa, professor, PhD; Song Mengting, master’s degree; Zhang Ke, associate research fellow, PhD, corresponding author, E-mail: kezhang@zzu.edu.cn; Wu Xuan, doctoral candidate.

Received date: 2024-07-19

  Revised date: 2024-10-15

  Online published: 2025-07-14

Supported by

This work is supported by the youth project of the National Natural Science Foundation of China titled “Innovation Requirement Panorama Identification and Innovation Scheme Optimization for Smart Product Service Driven by Data-Knowledge Fusion” (Grant No. 72401262), and the project of China Postdoctoral Science Fund titled “Multi Modal Data-Driven Intelligent Product Service Innovation Demand Perception and Innovation Solution Optimization”(Grant No. 2024M752968).

Fold

摘要

[目的/意义] 基于代际产品视角追溯用户需求的动态演变进而精准识别用户需求，对企业在竞争激烈的市场中进行产品迭代设计以谋求可持续发展至关重要。[方法/过程] 首先，运用隐含狄利克雷分布（Latent Dirichlet Allocation, LDA）主题模型从在线评论中提取代际产品的用户需求主题；其次，基于代际产品IPA-Kano模型中的需求变化模式研判关键用户需求；最后，基于目标产品代不同时间窗口下IPA-Kano模型中的特征变化模式，识别面向关键用户需求的产品细分特征并分析其改进方向。[结果/结论] 以Redmi K系列智能手机为例，研究结果展示了代际产品用户需求与产品细分特征的动态演化全景，研判出关键用户需求并对其细分特征进行改进分析，并通过对比产品参数的改进情况，验证所提方法的有效性。

关键词： 代际产品; 用户需求; 产品创新; 动态演化分析; 在线评论

本文引用格式

李尽法 , 宋孟亭 , 张珂 , 仵轩 . 代际产品视角下用户需求及产品细分特征的动态演化分析——基于在线评论挖掘[J]. 图书情报工作, 2025 , 69(14) : 73 -90 . DOI: 10.13266/j.issn.0252-3116.2025.14.007

Abstract

[Purpose/Significance] Tracing the dynamic evolution of user requirements from the perspective of intergenerational products and accurately identifying user requirements is crucial for enterprises to iteratively design products in a fiercely competitive market to pursue sustainable development. [Method/Process] Firstly, the Latent Dirichlet Allocation (LDA) topic model was used to extract user requirement themes for intergenerational products from online reviews. Secondly, key user requirements were identified based on the requirement change patterns in the intergenerational product IPA-Kano model. Finally, based on the feature change patterns in the IPA-Kano model under different time windows of the target product generation, product segmentation features targeting key user requirements were identified and their improvement directions were analyzed. [Result/Conclusion] Taking the Redmi K series smartphones as an example, the research results demonstrate the dynamic evolution of user requirements and product segmentation features across product generations. Key user requirements are identified, and their segmentation features are analyzed for improvement. The effectiveness of the proposed method is verified by comparing the improvement in product parameters.

Key words： intergenerational products; user requirements; product innovation; dynamic evolution analysis; online reviews

参考文献

[1] 赵敬华, 谢婉瑜, 吕锡婷, 等. 基于RF-BERT和UGC的用户需求识别及其发展趋势预测[J]. 情报科学, 2024, 42(1): 132-142. (ZHAO J H, XIE W Y, LV X T, et al. User demand identification and development trend prediction based on RF-BERT and UGC[J]. Information science, 2024, 42(1): 132-142.)
[2] 刘鹏, 闫煜析, 冯立杰, 等.用户需求导向下基于三级技术功效矩阵的产品创新机会识别[J]. 情报理论与实践, 2023, 46(8): 138-146. (LIU P, YAN Y X, FENG L J, et al. Product innovation opportunity identification based on three-level technology-effect matrix under user demand orientation[J]. Information studies: theory & application, 2023, 46(8): 138-146.)
[3] 王克勤, 刘朝明.基于在线评论的重要度绩效竞争对手分析的产品设计改进方法[J]. 计算机集成制造系统, 2022, 28(5): 1496-1506. (WANG K Q, LIU C M. Product design improvement based on importance performance competitor analysis of online reviews[J]. Computer integrated manufacturing systems, 2022, 28(5): 1496-1506.)
[4] 徐一钊, 闫宏斌, 马铁驹.竞争性环境下在线评论驱动的新产品开发：基于QFD的视角[J]. 系统工程, 2022, 40(5): 54-69. (XU Y Z, YAN H B, MA T J. Online review-driven new product development under competitive environments: a QFD perspective[J]. Systems engineering, 2022, 40(5): 54-69.)
[5] BISWAS S, ALI I, CHAKRABORTTY R K, et al. Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: a study of iPhone evolution[J]. Technological forecasting and social change, 2022, 178(C): 121549.
[6] BISWAS S, CHAKRABORTTY R K, TURAN H H, et al. Consideration of uncertainties in a dynamic modeling system integrated with a deep learning based forecasting approach[J]. CIRP journal of manufacturing science and technology, 2023, 44: 27-44.
[7] 裴卉宁, 刘鑫宇, 黄雪芹, 等.面向云服务平台的用户偏好产品属性动态获取方法[J]. 计算机集成制造系统, 2023, 29(11): 3774-3785. (PEI H N, LIU X Y, HUANG X Q, et al. Cloud service platform oriented dynamic acquisition method for user preference product attributes[J]. Computer integrated manufacturing systems, 2023, 29(11): 3774-3785.)
[8] DU Y, LIU D, DUAN H. A textual data-driven method to identify and prioritize user preferences based on regret/rejoicing perception for smart and connected products[J]. International journal of production research, 2022, 60(13): 4176-4196.
[9] CHEN T, PENG L, YANG J, et al. Analysis of user needs on downloading behavior of English vocabulary APPs based on data mining for online comments[J]. Mathematics, 2021, 9(12): 1341.
[10] WU I C, YU H K. Sequential analysis and clustering to investigate users’ online shopping behaviors based on need-states[J]. Information processing & management, 2020, 57(6): 102323.
[11] 纪雪, 高琦, 李先飞, 等.考虑产品属性层次性的评论挖掘及需求获取方法[J]. 计算机集成制造系统, 2020, 26(3): 747-759. (JI X, GAO Q, LI X F, et al. Reviews mining and requirements elicitation methodology considering product attributes' hierarchy[J]. Computer integrated manufacturing systems, 2020, 26(3): 747-759.)
[12] 王金凤, 仵轩, 冯立杰, 等. 用户偏好-制造商偏好双重视阈下的产品创新机会识别路径研究[J]. 计算机集成制造系统, 2024, 30(4): 1433-1445. (WANG J F, WU X, FENG L J, et al. Product innovation opportunity identification path under dual perspective of user preference and manufacturer preference[J]. Computer integrated manufacturing systems, 2024, 30(4): 1433-1445.)
[13] ZHANG K, LIN K Y, WANG J F, et al. UNISON framework for user requirement elicitation and classification of smart product-service system[J]. Advanced engineering informatics, 2023, 57: 101996.
[14] 王克勤, 王志义, 李靖, 等.面向产品设计改进的顾客偏好分析与预测[J]. 计算机集成制造系统, 2024, 30(6): 1989-2004. (WANG K Q, WANG Z Y, LI J, et al.Customer preference analysis and prediction for product design improvement[J]. Computer integrated manufacturing systems, 2024, 30(6): 1989-2004.)
[15] 杨德清, 张静, 郭伟, 等.基于在线产品社区的动态用户需求Kano模型构建研究[J]. 机械设计, 2018, 35(3): 12-19. (YANG D Q, ZHANG J, GUO W, et al.Research on dynamic Kano model construction of customer requirements based on online product community[J]. Journal of machine design, 2018, 35(3): 12-19.)
[16] SUN H, GUO W, SHAO H, et al. Dynamical mining of ever-changing user requirements: A product design and improvement perspective[J]. Advanced engineering informatics, 2020, 46: 101174.
[17] 于志刚, 成思源, 杨雪荣, 等.融合可拓学与在线评论挖掘的产品改进需求识别研究[J]. 智能系统学报, 2023, 18(5): 1047-1059. (YU Z G, CHENG S Y, YANG X R, et al.Product improvement demand identification incorporating topology and online comment mining[J]. CAAI transactions on intelligent systems, 2023, 18(5): 1047-1059)
[18] 翟运开, 卫东乐, 路薇, 等.考虑在线评论用户关注度的产品改进需求识别与分析——基于KANO-IPA模型[J]. 科技管理研究, 2023, 43(9): 95-104. (ZHAI Y K, WEI D L, LU W, et al. Identification and analysis of product improvement needs considering online review user attention: based on KANO-IPA model[J]. Science and technology management research, 2023, 43(9): 95-104.)
[19] 冯立杰, 关柯楠, 王金凤.基于在线评论考虑潜在用户需求的产品创新方案识别研究[J]. 情报理论与实践, 2022, 45(6): 129-137. (FENG L J, GUAN K N, WANG J F. Research on product innovation scheme identification considering potential user needs based on online comments[J]. Information studies: theory & application, 2022, 45(6): 129-137.)
[20] XIE J, BI Y, SHA Z, et al. Data-driven dynamic network modeling for analyzing the evolution of product competitions[J]. Journal of mechanical design, 2020, 142(3): 031112.
[21] 贾丹萍, 靳健, 耿骞, 等. 感性工学视角下的用户需求挖掘研究[J]. 情报学报, 2020, 39(3): 308-316. (JIA D P, JIN J, GENG Q, et al. A kansei engineering integrated approach for customer-needs mining from online product reviews[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(3): 308-316.)
[22] MEHRA A, RAJPUT S, PAUL J. Determinants of adoption of latest version smartphones: theory and evidence[J]. Technological forecasting and social change, 2022, 175(C): 121410.
[23] CHEN L, ZHANG P, LI S, et al. Growing pains: the effect of generational product innovation on mobile games performance[J]. Strategic management journal, 2022, 43(4): 792-821.
[24] MA J, KIM H M. Continuous preference trend mining for optimal product design with multiple profit cycles[J]. Journal of mechanical design, 2014, 136(6): 061002.
[25] 余佳琪, 赵豆豆, 刘蕤.在线健康社区慢性病患者评论主题情感协同挖掘研究——以甜蜜家园为例[J]. 数据分析与知识发现, 2023, 7(10): 95-108. (YU J Q, ZHAO D D, LIU R. Examining topics and sentiments of chronic disease patients' online reviews-case study of "sweet homeland"[J]. Data analysis and knowledge discovery, 2023, 7(10): 95-108.)
[26] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(Jan): 993-1022.
[27] 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.
[28] 叶琴, 蔡建峰, 张秋韵.基于隐含狄利克雷分配模型的企业创新测量方法研究[J]. 科技进步与对策, 2024, 41(2): 90-98. (YE Q, CAI J F, ZHANG Q Y. Measuring corporate innovation based on LDA topic model[J]. Science & technology progress and policy, 2024, 41(2): 90-98.)
[29] 刘自强, 岳丽欣, 冯志刚. 多维度视角下我国网络舆情热点话题演化特征研究[J]. 情报科学, 2024, 42(5): 85-93. (LIU Z Q, YUE L X, FENG Z G. The evolutionary characteristics of hot topics in China's internet public opinion from a multi-dimensional perspective[J]. Information science, 2024, 42(5): 85-93.)
[30] 刘泽, 单轸, 邵波.后疫情时代公众信息需求与公共图书馆信息服务策略研究[J]. 情报科学, 2023, 41(7): 179-188. (LIU Z, SHAN Z, SHAO B. Public information demand and public library information service strategies in the post epidemic era[J]. Information science, 2023, 41(7): 179-188.)
[31] 吴应强, 石乐怡, 张马萍.嬗变与发展：中国政府数据开放管理央地政策文本的内容主题挖掘与分析[J]. 情报科学, 2024, 42(4): 79-88. (WU Y Q, SHI L Y, ZHANG M P. Transmutation and development: content theme mining and analysis of central and local policy texts on open government data management in China[J]. Information science, 2024, 42(4): 79-88.)
[32] 李慧, 胡吉霞, 佟志颖.面向多源数据的学科主题挖掘与演化分析[J]. 数据分析与知识发现, 2022, 6(7): 44-55. (LI H, HU J X, TONG Z Y. Subject topic mining and evolution analysis with multi-source data[J]. Data analysis and knowledge discovery, 2022, 6(7): 44-55.)
[33] 毕达天, 孔婧媛, 米艳霖, 等.UGC跨平台投放对受众信息行为的影响研究[J]. 图书情报工作, 2023, 67(16): 76-87. (BI D T, KONG J Y, MI Y L, et al. Research on the impact of UGC cross-platform delivery on audience information behavior[J]. Library and information service, 2023, 67(16): 76-87.)
[34] 易明, 姚玉佳, 李佳奇, 等.不同就医情境下在线健康社区用户信息需求偏好研究——以寻医问药网“有问必答”社区为例[J]. 情报科学, 2023, 41(1): 9-16, 79. (YI M, YAO Y J, LI J Q, et al. Information demand preferences of online health community users in different medical treatment situations: taking the "ask questions and answers" community of Xunyaoqiu.com as an example[J]. Information science, 2023, 41(1): 9-16, 79.)
[35] 路薇, 高盼, 翟运开.带有反馈调节的远程医疗专家自适化推荐[J]. 系统管理学报, 2023, 32(5): 960-975. (LU W, GAO P, ZHAI Y K. An adaptive recommendation method for telemedicine specialists with feedback adjustment[J]. Journal of systems & management, 2023, 32(5): 960-975.)
[36] 张柳, 王慧, 相甍甍.基于LDA的突发事件应急管理主题热度与演化分析[J]. 情报科学, 2023, 41(6): 182-191. (ZHANG L, WANG H, XIANG M M. Analysis on the topic popularity and evolution of emergency management based on LDA[J]. Information science, 2023, 41(6): 182-191.)
[37] MARTILLA J A, JAMES J C. Importance-performance analysis[J]. Journal of marketing, 1977, 41(1): 77-79.
[38] 毕达天, 王璐, 王雨菲, 等. AIGC产品用户需求特征体系构建及改进策略研究[J]. 图书情报工作, 2024, 68(14): 14-24. (BI D T, WANG L, WANG Y F, et al. Research on the construction of user demand characteristic system and improvement strategy of AIGC products[J]. Library and information service, 2024, 68(14): 14-24.)
[39] 孙玲玲, 胡彦蓉, 刘洪久.基于LSTM-LDA算法和IPA分析的在线品牌社群用户关注热点研究[J]. 情报杂志, 2021, 40(9): 178-186. (SUN L L, HU Y R, LIU H J. LSTM and LDA fusion algorithm analysis of online brand community for users' focus hot spots[J]. Journal of intelligence, 2021, 40(9): 178-186.)
[40] KANO N. Attractive quality and must-be quality[J]. Journal of the Japanese society for quality control, 1984, 31(4): 147-156.
[41] 朱红涛, 朱梦真, 王思源, 等.面向用户需求的高校未来学习中心服务优化研究[J]. 图书情报工作, 2024, 68(14): 25-33. (ZHU H T, ZHU M Z, WANG S Y, et al. Research on service optimization in university future learning centers on user needs[J]. Library and information service, 2024, 68(14): 25-33.)
[42] 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. (ZHAO Y Q, RUAN P N, LIU X Y, et al. Study on user satisfaction evaluation based on online comment[J]. Management review, 2020, 32(3): 179-189.)
[43] 蒋昀霖, 夏志杰.基于KANO-IPA模型的用户满意度分析——以人机交互体验的智能个人助理产品的在线评论为例[J]. 科技与经济, 2024, 37(4): 106-110. (JIANG Y L, XIA Z J. Users satisfaction analysis based on KANO-IPA model: taking online review of intelligent personal assistant product in human-computer interaction as an example[J]. Science & technology and economy, 2024, 37(4): 106-110.)
[44] WANG Y, LI X. Mining product reviews for needs-based product configurator design: a transfer learning-based approach[J]. IEEE Transactions on industrial informatics, 2020, 17(9): 6192-6199.
[45] TSENG C C. An IPA-Kano model for classifying and diagnosing airport service attributes[J]. Research in transportation business & management, 2020, 37(Dec): 100499.
[46] LU H, RUI X, GEMECHU G F, et al. Quantitative evaluation of psychological tolerance under the haze: a case study of typical provinces and cities in China with severe haze[J]. International journal of environmental research and public health, 2022, 19(11): 6574.
[47] 王婷, 杨文忠.文本情感分析方法研究综述[J]. 计算机工程与应用, 2021, 57(12): 11-24. (WANG T, YANG W Z. Review of text sentiment analysis methods[J]. Computer engineering and applications, 2021, 57(12): 11-24.)
[48] 王雪, 董庆兴, 张斌.面向在线评论的用户需求分析框架与实证研究——基于KANO模型[J]. 情报理论与实践, 2022, 45(2): 160-167. (WANG X, DONG Q X, ZHANG B. Analytical framework and empirical study of user needs for online reviews based on KANO model[J]. Information studies: theory & application, 2022, 45(2): 160-167.)
[49] 董亚男, 秦思奇, 相宇晗, 等.考虑竞品信息的顾客满意度研究[J]. 计算机集成制造系统, 2024, 30(9): 3274-3287. (DONG Y N, QIN S Q, XIANG Y H, et al. Customer satisfaction measurement considering competitive products information[J]. Computer integrated manufacturing systems, 2024, 30(9): 3274-3287.)
[50] 王克勤, 高智姣, 乔亚楠, 等.在线评论中的用户需求识别及其演化趋势挖掘[J]. 机械科学与技术, 2023, 42(7): 1070-1080. (WANG K Q, GAO Z J, QIAO Y N, et al. User requirements identification and trend of evolution mining in online reviews[J]. Mechanical science and technology for aerospace engineering, 2023, 42(7): 1070-1080.)
[51] 张丽, 张祯.基于文本挖掘的新冠肺炎疫情下医药在线消费者的需求研究[J]. 运筹与管理, 2024, 33(8): 184-190. (ZHANG L, ZHANG Z. Research on the demand of online pharmaceutical consumers under the COVID-19 based on text mining[J]. Operations research and management science, 2024, 33(8): 184-190.)
[52] HANNIGAN T R, HAANS R F J, VAKILI K, et al. Topic modeling in management research: rendering new theory from textual data[J]. Academy of management annals, 2019, 13(2): 586-632.
Options
文章导航

/

〈 〉