华东师范大学学报(自然科学版) ›› 2022, Vol. 2022 ›› Issue (5): 100-114.doi: 10.3969/j.issn.1000-5641.2022.05.009

• 供应链知识图谱构建与分析 • 上一篇    

疫情背景下快递物流服务的用户行为画像及主题挖掘研究

李继玲1,*(), 李宝林2, 严宋如1   

  1. 1. 西北政法大学 市场营销系, 西安 710122
    2. 成都信息工程大学 电子商务系, 成都 610103
  • 收稿日期:2022-07-13 接受日期:2022-07-14 出版日期:2022-09-25 发布日期:2022-09-26
  • 通讯作者: 李继玲 E-mail:345196182@qq.com
  • 基金资助:
    四川省科技服务业示范项目 (2021GFW015); 四川省电子商务与现代物流研究中心重点项目(DSWL21-3)

Research on user behavior portrait and subject mining in the express logistics field during Coronavirus epidemic

Jiling LI1,*(), Baolin LI2, Songru YAN1   

  1. 1. Department of Marketing, Northwest University of Political Science and Law, Xi’an 710122, China
    2. Department of Electronic Commerce, Chengdu University of Information Technology, Chengdu 610103, China
  • Received:2022-07-13 Accepted:2022-07-14 Online:2022-09-25 Published:2022-09-26
  • Contact: Jiling LI E-mail:345196182@qq.com

摘要:

基于微博2019年11月11日—2022年5月12日的快递物流博文数据, 对疫情背景下快递物流服务的用户行为进行画像, 以扎根理论为理论框架, 结合抽象聚类方法抽象出5种用户行为、22个主题内容, 并生成相应的用户画像. 论文进一步探讨了主题的内容、主题的演化和群体的差异性. 结果表明: ① 用户对快递物流服务的满意行为单一; ② 用户的不满意行为多样化, 存在明显的升级性; ③ “运输效率”和“物流保障”是影响快递物流服务评价的主要因素; ④ 疫情的发展变化影响用户主题内容呈现的关注点和态度;⑤ 主题内容具有明显不同程度的群体差异.

关键词: 用户行为画像, 主题挖掘, 演化, 群体差异

Abstract:

Based on logistics-field blog post data from Weibo from November 2019 to May 2022, the user behaviors of express logistics services in the context of the Coronavirus epidemic are profiled. Using grounded theory and abstract clustering methods, five user behaviors and 22 subject contents are abstracted, and the corresponding user profile is generated. This paper further discusses the subject contents, the subject evolution, and the analysis of group differences. The results show that user satisfaction with logistics services was similar, and the dissatisfaction was diversified with obvious escalation. Variables of transportation efficiency and logistics guarantee were the main factors affecting the evaluation, and the development of the epidemic affected the concerns and attitudes of the subject contents, which had obvious group differences at different degrees.

Key words: user behavior profile, subject mining, evolution, group differences

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