Social Media Sentiment Analysis for Enhancing Demand Forecasting Models Using Machine Learning Models.

Abstract

Accurate demand forecasting is critical for effective inventory management, production planning, and overall organizational efficiency. Traditional forecasting methods, which typically rely on historical sales data and economic indicators, often fall short in capturing the dynamic nature of consumer behavior and market trends. This study investigates the integration of sentiment analysis from social media with machine learning techniques to enhance demand forecasting accuracy. By analyzing real-time consumer sentiments expressed on social media, the proposed model aims to provide more responsive and precise demand predictions. The research reviews the limitations of conventional forecasting approaches and highlights the potential of incorporating sentiment analysis. A comprehensive methodology for extracting and analyzing sentiment from social media data is proposed, followed by its integration into demand forecasting models. Empirical results demonstrate that the inclusion of sentiment analysis significantly improves forecast accuracy over traditional methods. This study underscores the benefits of leveraging social media sentiment for demand forecasting while acknowledging challenges related to data quality, linguistic complexity, and contextual interpretation. Ultimately, integrating sentiment analysis with machine learning presents a promising advancement for more adaptive and accurate demand forecasting across various industries.