Real-Time Dynamic Pricing Using Machine Learning: Integrating Customer Sentiment and Predictive Models for E-Commerce

Dynamic pricing has emerged as a crucial strategy for e-commerce platforms to maximize profitability while remaining competitive in rapidly changing digital markets. Traditional pricing methods often fail to capture the complexity of customer behavior and the rapid evolution of market trends. To address these limitations, this study introduces a machine learning based framework that integrates transactional, behavioral, and contextual data with multilingual sentiment analysis from customer reviews. The framework employs multiple algorithms, including Random Forest, Gradient Boosting, Neural Networks, and XGBoost, with extensive feature engineering and model evaluation. Experimental results on a large-scale retail and e-commerce dataset show that the proposed XGBoost-based approach achieved superior performance, with a Mean Absolute Error (MAE) of 1.29, Root Mean Squared Error (RMSE) of 1.65, and an R² of 0.97, significantly outperforming baseline models. These findings underscore the framework's capacity to facilitate real-time, adaptive, and customer-centric pricing mechanisms. The study contributes by presenting 1) an end-to-end ML pipeline for dynamic pricing, 2) the novel incorporation of sentiment-based features into predictive models, and 3) a comparative evaluation that establishes XGBoost as the most effective model. The results demonstrate both practical and theoretical value, offering insights for e-commerce platforms seeking to optimize revenue and ensure pricing fairness in real-world scenarios.