Abstract
The growth of digital media usage has accelerated the development of big data technology. According to the agenda-setting theory, news media inform the public regarding major agendas and business cycles. This study investigated 168,786 news documents from 2016 to 2020 related the South Korea fashion business using Python. A total of 19 topics were extracted through latent Dirichlet allocation and then transformed into structured data using a time series approach to analyze significant changes in trends. The results indicate that major fashion industry topics include business management strategies to increase sales, diversification of the retail structure, influence of CEOs, and merchandise marketing activities. Thereafter, statistically significant hot and cold topics were derived to identify the shifts in topic themes. This study expands the fashion business contexts with agenda-setting theory through big data time series analyses and can be referenced for the government agencies to support fashion industry policies.
| Original language | English |
|---|---|
| Article number | e18048 |
| Journal | Heliyon |
| Volume | 9 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Authors
Keywords
- Agenda-setting theory
- Fashion industry news
- Latent dirichlet allocation
- Time series regression analysis
- Topic modeling