Abstract
Standard Industrial Classification (SIC) classify organizations based on their business activities. However, choosing appropriate SIC code that represents an organization’s business activities in a challenging task. In the UK, there are almost 100 categories each having several subcategories of predefined business activities designed by experts. However, such scheme cannot cater for emerging business needs while some organizations cannot be easily defined by a single SIC code, due to the complexity of their business nature. Similarly, if a company expands or changes its operation during the year, a new SIC code needs to be assigned. This results in organizations having difficulties picking representative SIC code to use in defining their business activities. In this paper, we propose a dynamic framework that can automatically group organizations based on their business activities. Our framework leverages techniques from topic modelling. Result shows that our proposed framework can automatically adapt to changing business needs and cluster organizations effectively.
Original language | English |
---|---|
Title of host publication | Proceedings of the 19th International Conference on Electronic Business : ICEB, Newcastle upon Tyne, U.K., December 8-12 |
Editors | Eldon Y. LI, Honglei LI |
Publisher | Association for Information Systems |
Pages | 570-572 |
Number of pages | 3 |
Volume | 2019-December |
Publication status | Published - Dec 2019 |
Event | 19th International Conference on Electronic Business, ICEB 2019 - Newcastle upon Tyne, United Kingdom Duration: 8 Dec 2019 → 12 Dec 2019 |
Publication series
Name | Proceedings of International Conference on Electronic Business (ICEB) |
---|---|
Volume | 2019 |
ISSN (Print) | 1683-0040 |
Conference
Conference | 19th International Conference on Electronic Business, ICEB 2019 |
---|---|
Country/Territory | United Kingdom |
City | Newcastle upon Tyne |
Period | 8/12/19 → 12/12/19 |
Funding
This work is supported by the Office of National Statistics, UK.
Keywords
- Big data analytics
- Longitudinal analysis
- Standard industrial classification
- Topic modelling