Understanding the Food Supply Chain using Social Media Data Analysis

Shukla, N; Mishra, N; Singh, A

Understanding the Food Supply Chain using Social Media Data Analysis

Shukla, N Mishra, N Singh, A

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Publisher:: IARIA
Publication Type:: Conference Proceeding
Citation:: 7th International Conference on Advances in Information Mining and Management (IMMM), 2017
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Shukla, N	en_US
dc.contributor.author	Mishra, N	en_US
dc.contributor.author	Singh, A	en_US
dc.date	2017-06-25	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	7th International Conference on Advances in Information Mining and Management (IMMM), 2017	en_US
dc.identifier.isbn	978-1-61208-566-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126480
dc.description.abstract	This paper proposes a big data analytics based approach, which considers social media (Twitter) data for identifying supply chain management issues in food in-dustries. In particular, the proposed approach includes: (i) capturing of relevant tweets based on keywords; (ii) pre-processing of the raw tweets; and, (iii) text analysis using support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included cluster of words, which can inform supply chain (SC) decision makers about the customer feedback and issues in the flow/quality of the food products. A case study of the beef supply chain was analysed using the proposed approach where three weeks of data from Twitter was used. The results indicated that the proposed text analytic approach can be helpful to efficiently identify and summarise crucial customer feedback for supply chain management.	en_US
dc.publisher	IARIA	en_US
dc.relation.ispartof	7th International Conference on Advances in Information Mining and Management (IMMM)	en_US
dc.relation.ispartof	International Conference on Advances in Information Mining and Management	en_US
dc.title	Understanding the Food Supply Chain using Social Media Data Analysis	en_US
dc.type	Conference Proceeding
utslib.location.activity	Venice, Italy	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2017-06-29	en_US
pubs.start-date	2017-06-25	en_US

Abstract:

This paper proposes a big data analytics based approach, which considers social media (Twitter) data for identifying supply chain management issues in food in-dustries. In particular, the proposed approach includes: (i) capturing of relevant tweets based on keywords; (ii) pre-processing of the raw tweets; and, (iii) text analysis using support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included cluster of words, which can inform supply chain (SC) decision makers about the customer feedback and issues in the flow/quality of the food products. A case study of the beef supply chain was analysed using the proposed approach where three weeks of data from Twitter was used. The results indicated that the proposed text analytic approach can be helpful to efficiently identify and summarise crucial customer feedback for supply chain management.

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http://hdl.handle.net/10453/126480