Akshit Singh
Social media data analytics to improve supply chain management in food industries
Singh, Akshit; Shukla, Nagesh; Mishra, Nishikant
Authors
Nagesh Shukla
Professor Nishikant Mishra Nishikant.Mishra@hull.ac.uk
Professor/ Head of Management Systems Subject Group
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.
Citation
Singh, A., Shukla, N., & Mishra, N. (2018). Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review, 114, 398-415. https://doi.org/10.1016/j.tre.2017.05.008
Journal Article Type | Article |
---|---|
Acceptance Date | May 16, 2017 |
Online Publication Date | Jun 9, 2017 |
Publication Date | 2018-06 |
Deposit Date | Jun 11, 2017 |
Journal | Transportation research. Part E, Logistics and transportation review |
Electronic ISSN | 1366-5545 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 114 |
Pages | 398-415 |
DOI | https://doi.org/10.1016/j.tre.2017.05.008 |
Keywords | Beef supply chain, Twitter data, Sentiment analysis |
Public URL | https://hull-repository.worktribe.com/output/452227 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S1366554516303817 |
Additional Information | This is a description of an article which has been published in: Transportation research. Part E, Logistics and transportation review, 2017 |
Contract Date | Jun 11, 2017 |
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