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Understanding the Food Supply Chain Using Social Media Data Analysis

Authors:
Nagesh Shukla
Nishikant Mishra
Akshit Singh

Keywords: Twitter data; social media; data mining; clustering.

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.

Pages: 28 to 34

Copyright: Copyright (c) IARIA, 2017

Publication date: June 25, 2017

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-566-1

Location: Venice, Italy

Dates: from June 25, 2017 to June 29, 2017