An explainable ensemble machine learning approach for multi-domain, multiclass sentiment analysis in Amazon product reviews

Sentiment analysis (SA) of online reviews is pivotal for e-commerce platforms yet challenges such as massive user-generated content volumes and class imbalances hinder accurate multiclass predictions and model interpretability. This study introduces a novel explainable ensemble learning framework for multiclass SA (positive, neutral, negative) across three Amazon product domains: appliances, groceries, and clothing. The framework integrates diverse supervised classifiers in a stacking ensemble, with SHapley Additive exPlanations (SHAP) innovatively employed not only to elucidate feature contributions but also to rank and interpret the individual impacts of base classifiers on ensemble predictions, a pioneering application in domain-specific SA, as it enables global insights into model dynamics and base model selection, addressing gaps in prior studies that relied on local explanations like LIME (Local Interpretable Model-agnostic Explanations). Evaluated using imbalance-sensitive metrics (weighted/macro F1-score, Matthews Correlation Coefficient, Cohen’s Kappa, Geometric Mean), the ensemble surpasses individual classifiers and demonstrates higher macro F1 and G-Mean than the transformer-based ALBERT model, while ALBERT excels in weighted F1, MCC, and Cohen's Kappa. Extra Trees notably excelled in the G-Mean for minority classes. SHAP analysis uncovers domain-specific drivers and base model roles, enhancing transparency. The results underscore the framework’s efficacy in delivering robust performance and actionable insights for trust modelling, automated analytics, and personalized recommendations. This work lays the groundwork for extensions to low-resource domains, multimodal data, and finer rating scales, advancing interpretable SA in e-commerce.