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Social media data analytics to improve supply chain management in food industries
Authors
N Mishra
N Shukla
A Singh
Publication date
1 June 2018
Publisher
'Elsevier BV'
Doi
Cite
Abstract
© 2017 Elsevier Ltd This paper proposes a big-data analytics-based approach that considers social media (Twitter) data for the identification of supply chain management issues in food industries. In particular, the proposed approach includes text analysis using a support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included a cluster of words which could inform supply-chain (SC) decision makers about customer feedback and issues in the flow/quality of food products. A case study in the beef supply chain was analysed using the proposed approach, where three weeks of data from Twitter were used
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OPUS - University of Technology Sydney
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Last time updated on 20/04/2021