AI-Driven Multi-Modal Demand Forecasting: Combining Social Media Sentiment with Economic Indicators and Market Trends

Abstract

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.