Abstract
The purpose of this study is to develop and demonstrate a semi-automated text analytics approach for the identification and categorization of information that can be used for country logistics assessments. In this paper, we develop the methodology on a set of documents for 21 countries using machine learning techniques while controlling both for 4 different time periods in the world FDI trends, and the different geographic and economic country affiliations. We report illustrative findings followed by a presentation of the separation of concerns/division of labor between the domain expert and the text analyst. Implications are discussed and future work is outlined.
Original language | English |
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Title of host publication | Dynamics in Logistics : Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany |
Editors | Michael Freitag, Herbert Kotzab, Jürgen Pannek |
Number of pages | 11 |
Place of Publication | Cham |
Publisher | Springer |
Publication date | 2017 |
Pages | 119-129 |
Chapter | 11 |
ISBN (Print) | 9783319451169 |
ISBN (Electronic) | 9783319451176 |
DOIs | |
Publication status | Published - 2017 |
Event | The 5th International Conference on Dynamics in Logistics. LDIC 2016 - Bremen, Germany Duration: 22 Feb 2016 → 25 Feb 2016 Conference number: 5 http://www.ldic-conference.org/ |
Conference
Conference | The 5th International Conference on Dynamics in Logistics. LDIC 2016 |
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Number | 5 |
Country/Territory | Germany |
City | Bremen |
Period | 22/02/2016 → 25/02/2016 |
Internet address |
Series | Lecture Notes in Logistics |
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ISSN | 2194-8917 |
Keywords
- Logistics
- Transport system evaluation
- Big data analytics
- Data mining
- Text mining
- Machine learning