Wedi'i Gadw mewn:
| Prif Awduron: | , , , , |
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| Fformat: | Recurso digital |
| Iaith: | |
| Cyhoeddwyd: |
Zenodo
2024
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| Pynciau: | |
| Mynediad Ar-lein: | https://doi.org/10.5281/zenodo.19689512 |
| Tagiau: |
Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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Tabl Cynhwysion:
- <p class="p1">This study investigates the AI-Driven Multi-Modal Demand Forecasting: Combining Social Media Sentiment with Economic Indicators and Market Trends. By leveraging the vast amount of user-generated content on social media platforms, we aim to improve the accuracy and responsiveness of traditional demand forecasting methods. The research employs a comprehensive methodology, including data collection from multiple social media sources, advanced natural language processing techniques for sentiment analysis, and state-of-the-art machine learning models for demand prediction. Results demonstrate a statistically significant improvement in forecasting accuracy when incorporating sentiment analysis, particularly in volatile market conditions. This paper contributes to the growing body of knowledge on data-driven decision-making in supply chain management and offers practical insights for businesses seeking to enhance their demand forecasting capabilities.</p>