Social media data analytics to improve supply chain management in food industries

Singh, Akshit ORCID: 0000-0001-6498-4190, Shukla, Nagesh and Mishra, Nishikant (2018) Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review, 114. pp. 398-415.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Beef supply chain, Twitter data, Sentiment analysis
Depositing User:	Symplectic Admin
Date Deposited:	13 Aug 2018 07:06
Last Modified:	19 Jan 2023 01:29
DOI:	10.1016/j.tre.2017.05.008
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3024819