E-commerce web content is dynamic and diverse, necessitating continuous monitoring and adaptation. This presents researchers with the challenge of discovering methods to improve delivered services. Hence, integrating natural language processing (NLP), Machine Learning (ML), Deep Learning (DL), and sentiment analysis (SA) provides businesses with robust frameworks to utilize customer feedback and enhance decision-making. Therefore, we introduce a novel dataset collection methodology that captures the dynamic relationships between e-commerce web content and consumer sentiment. Additionally, we introduce a novel, real-consumer-based quality metric on product content through FG-CSrP, extending SA into a new Fine-Grained Consumer Sentiment related to the Product. We evaluated our dataset using baseline models: Deep Neural Network (DNN), Long Short-Term Memory (LSTM), DistilBERT, and twelve automatically optimized models created by AutoGluon-Tabular across three scenarios, each with varying feature inputs (numerical, textual, and both). We then applied Explainable Artificial Intelligence (XAI) to the DNN model to explain feature importance in prediction. Our findings showed that LightGBMXT outperformed the other models in two out of three scenarios, and XAI interpretations highlighted the significant role of vendor-provided web content details in consumer sentiment. Overall, our approach provides actionable insights that can help vendors improve e-commerce strategies and strengthen customer engagement.

The increasing reliance on e-commerce services has generated extensive product-related information and customer opinions, enabling analysis of product-page evolution and consumer sentiment. However, repeated product-page observations do not directly represent temporal changes without extracting differences between snapshots. This study proposes a difference-based dynamic feature extraction methodology using time-normalized numerical differences and TF-IDF cosine distance. The extracted features were integrated with original product-related features and evaluated using tuned LightGBM to predict the dominant Fine-Grained Consumer Sentiment related to the Product (FG-CSrP) class at later observations of previously observed products. Performance was assessed through expanding temporal validation windows and an independent temporal holdout. LightGBM gain-based and group-level permutation importance were further used to examine feature contributions. On the final holdout, Baseline and Original + Dynamic settings achieved Macro-F1 scores of 0.8710 and 0.8616, respectively, showing no clear improvement from dynamic features. Additional benchmark and robustness analyses highlighted temporal stability, limited generalization to unseen products, and superior Macro-F1 performance of LightGBM compared with regularized logistic regression. LightGBM gain-based and group-level permutation importance analyses showed that original product-related features contributed most to the predictive performance. Overall, difference-based features explicitly represent changes in evolving e-commerce content, although these features did not demonstrate additional predictive gains.