Details
| Original language | English |
|---|---|
| Article number | 2348 |
| Journal | Electronics (Switzerland) |
| Volume | 10 |
| Issue number | 19 |
| Publication status | Published - 25 Sept 2021 |
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.
Keywords
- AI, Context-aware sentiment analysis, Social media, Supply chain management
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Hardware and Architecture
- Computer Science(all)
- Computer Networks and Communications
- Engineering(all)
- Electrical and Electronic Engineering
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In: Electronics (Switzerland), Vol. 10, No. 19, 2348, 25.09.2021.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Artificial Intelligence, Social Media and Supply Chain Management
T2 - The Way Forward
AU - Khatua, Apalak
AU - Khatua, Aparup
AU - Chi, Xu
AU - Cambria, Erik
N1 - Funding Information: Funding: This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046).
PY - 2021/9/25
Y1 - 2021/9/25
N2 - 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.
AB - 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.
KW - AI
KW - Context-aware sentiment analysis
KW - Social media
KW - Supply chain management
UR - http://www.scopus.com/inward/record.url?scp=85115745846&partnerID=8YFLogxK
U2 - 10.3390/electronics10192348
DO - 10.3390/electronics10192348
M3 - Review article
AN - SCOPUS:85115745846
VL - 10
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
SN - 2079-9292
IS - 19
M1 - 2348
ER -