Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

Apalak Khatua,Aparup Khatua,Xu Chi,Erik Cambria

doi:10.3390/electronics10192348

Abstract

Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency.

Highlights

Social media platforms allow customers to comment about products, services, brands, or companies [1]
We argue that the scope of sentiment analysis and opinion mining in the context of Supply chain management (SCM) can have a wide-range of applications
We need to explore whether context-aware sentiment analysis would create value for SCM

Summary

Introduction

Social media platforms allow customers to comment about products, services, brands, or companies [1]. It is worth noting that extracting meaningful information, such as the sentiment, emotion, or opinion of stakeholders, from these social media data is not a trivial AI task due to the voluminous nature of the data and the presence of irrelevant or junk data. These noisy data ‘should be verified so that good data could be picked out’ to accurately extract the sentiments of the stakeholders, especially in the context of supply chains [5,6].

Objectives

Results

Discussion

Conclusion