AI-Driven Multi-Modal Demand Forecasting: Combining Social Media Sentiment with Economic Indicators and Market Trends
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2024
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| _version_ | 1866901997423165440 |
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| author | Singh, Ravi Kumar Nayani, Aravind Reddy Vaidya, Harsh Gupta, Alok Selvaraj, Prassanna |
| author_facet | Singh, Ravi Kumar Nayani, Aravind Reddy Vaidya, Harsh Gupta, Alok Selvaraj, Prassanna |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19689512 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Driven Multi-Modal Demand Forecasting: Combining Social Media Sentiment with Economic Indicators and Market Trends Singh, Ravi Kumar Nayani, Aravind Reddy Vaidya, Harsh Gupta, Alok Selvaraj, Prassanna Sentiment Analsis Demand Forecasting Machine Learning Social Media Analysis Natural Language Processing Time Series Analysis Supply Chain Management <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> |
| title | AI-Driven Multi-Modal Demand Forecasting: Combining Social Media Sentiment with Economic Indicators and Market Trends |
| topic | Sentiment Analsis Demand Forecasting Machine Learning Social Media Analysis Natural Language Processing Time Series Analysis Supply Chain Management |
| url | https://doi.org/10.5281/zenodo.19689512 |