Understanding the Food Supply Chain using Social Media Data Analysis

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