Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments

Aseem Kinra; Raghava Rao Mukkamala; Ravi Vatrapu

doi:10.1007/978-3-319-45117-6_11

Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments

Aseem Kinra, Raghava Rao Mukkamala, Ravi Vatrapu

Institut for Produktion og Erhvervsøkonomi

Westerdals – Oslo School of Arts, Communication and Technology

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › peer review

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.

Originalsprog	Engelsk
Titel	Dynamics in Logistics : Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany
Redaktører	Michael Freitag, Herbert Kotzab, Jürgen Pannek
Antal sider	11
Udgivelsessted	Cham
Forlag	Springer
Publikationsdato	2017
Sider	119-129
Kapitel	11
ISBN (Trykt)	9783319451169
ISBN (Elektronisk)	9783319451176
DOI	https://doi.org/10.1007/978-3-319-45117-6_11
Status	Udgivet - 2017
Begivenhed	The 5th International Conference on Dynamics in Logistics. LDIC 2016 - Bremen, Tyskland Varighed: 22 feb. 2016 → 25 feb. 2016 Konferencens nummer: 5 http://www.ldic-conference.org/

Konference

Konference	The 5th International Conference on Dynamics in Logistics. LDIC 2016
Nummer	5
Land/Område	Tyskland
By	Bremen
Periode	22/02/2016 → 25/02/2016
Internetadresse	http://www.ldic-conference.org/

Navn	Lecture Notes in Logistics
ISSN	2194-8917

Emneord

Logistics
Transport system evaluation
Big data analytics
Data mining
Text mining
Machine learning

Adgang til dokumentet

10.1007/978-3-319-45117-6_11Licens: Ikke-specificeret

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Citationsformater

Kinra, Aseem ; Mukkamala, Raghava Rao ; Vatrapu, Ravi. / Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments. Dynamics in Logistics: Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany. red. / Michael Freitag ; Herbert Kotzab ; Jürgen Pannek. Cham : Springer, 2017. s. 119-129 (Lecture Notes in Logistics).

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title = "Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments",

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.",

keywords = "Logistics, Transport system evaluation, Big data analytics, Data mining, Text mining, Machine learning, Logistics, Transport system evaluation, Big data analytics, Data mining, Text mining, Machine learning",

author = "Aseem Kinra and Mukkamala, {Raghava Rao} and Ravi Vatrapu",

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note = "The 5th International Conference on Dynamics in Logistics. LDIC 2016 ; Conference date: 22-02-2016 Through 25-02-2016",

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Kinra, A , Mukkamala, RR & Vatrapu, R 2017, Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments. i M Freitag, H Kotzab & J Pannek (red), Dynamics in Logistics: Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany. Springer, Cham, Lecture Notes in Logistics, s. 119-129, The 5th International Conference on Dynamics in Logistics. LDIC 2016, Bremen, Tyskland, 22/02/2016. https://doi.org/10.1007/978-3-319-45117-6_11

Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments. / Kinra, Aseem ; Mukkamala, Raghava Rao ; Vatrapu, Ravi.
Dynamics in Logistics: Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany. red. / Michael Freitag; Herbert Kotzab; Jürgen Pannek. Cham: Springer, 2017. s. 119-129 (Lecture Notes in Logistics).

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › peer review

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N2 - 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.

AB - 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.

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KW - Big data analytics

KW - Data mining

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KW - Machine learning

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KW - Transport system evaluation

KW - Big data analytics

KW - Data mining

KW - Text mining

KW - Machine learning

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