| Issue |
E3S Web Conf.
Volume 409, 2023
International Conference on Management Science and Engineering Management (ICMSEM 2023)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 13 | |
| Section | Data Science | |
| DOI | https://doi.org/10.1051/e3sconf/202340903005 | |
| Published online | 01 August 2023 | |
Study on the Spatial and Temporal Characteristics of Weibo Users’ Online Shopping Festival Concerns: A Case Study of “Double 11 Shopping Carnival”
1 Business School, Sichuan University, Chengdu 610065, People’s Republic of China
2 Department of Mathematics and Computer Science, University of Munster, Munster, Germany
* e-mail: L_iu117@163.com
Online shopping festivals have become powerful ways for major e-commerce platforms to conduct promotions. To explore the rule and hidden information of the change of users’ attention to online shopping festivals, it can provide scientific and effective marketing reference, and promote the sustainable development of online shopping festivals. This paper takes the “Double 11” online shopping festival as the research object. Based on the LDA+LSA+NMF integrated theme extraction model, kernel density analysis, grouping analysis and cluster outlier analysis in ArcGIS technology are adopted. We explore the evolution pattern of microblog users’ attention to the online shopping festival in the spatial and temporal dimensions. In the temporal dimension, the number of releases showed a stepwise increase, and concerns showed annual topic evolution and daily three-stage differential evolution. In the spatial dimension, the release volume showed a gradient decreasing trend from coastal to inland and from east to west, and concerns showed certain regional differences. The main contribution of this paper is to propose a funnel theme extraction model with multi-model integration, and provide a new perspective for the spatial research of online shopping festival based on ArcGIS spatial analysis technology.
Key words: Online shopping festival / Theme analysis / Spatial-temporal feature analysis / Text mining
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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