Analyzing Customer Behavior and Sales Trends in Walmart Stores Using Sentiment Analysis and Predictive Modeling

This paper presents a data-driven analysis of Walmart’s sales performance and customer behavior by examining transaction records and sentiment data. Covering multiple weeks across diverse store locations, the study investigates how customer sentiment—extracted through natural language processing—correlates with fluctuations in weekly sales. It also explores the impact of external factors such as holidays and temperature. Through predictive modeling techniques, including regression and machine learning algorithms, the research aims to forecast sales trends and uncover actionable insights for improving strategic planning and customer satisfaction in retail operations.