Deep Reinforcement Learning for Ethically‐Aware Personal‎ized Sentiment Analysis of Customer Reviews

A conventional sentiment analyzer is static, labeled corpora and risk amplifying biases entrenched in its training data. To overcome these ‎limitations, we introduce a multi‐agent, aspect‐based deep reinforcement learning framework, the ADRSA algorithm, which continuously ‎adapts to streaming customer reviews while enforcing ethical personalization. Each agent specializes in one product aspect, for example, price, ‎food, product, and updates its policy network (GRU + policy head) in real time, receiving composite rewards for both classification accuracy ‎and adherence to fairness constraints. We evaluated ADRSA on 50K real‐world reviews (Amazon Electronics, Books, Home & Kitchen) ‎with anonymized age, gender, and region metadata. On the “price” aspect (APA), ADRSA attains 0.75 precision, 0.72 recall, 0.78 F1 and ‎‎0.88 accuracy; on “food” (AFR), 0.74 precision, 0.60 recall, 0.65 F1 and 0.89 accuracy, and on “product” (APR), 0.75 precision, 0.62 re-‎call, 0.65 F1 and an exceptional 0.98 accuracy, outperforming static LSTM (max 0.91 acc) and RNMS and SVM baselines by up to 7 per-‎centage points in F1. These results demonstrate ADRSA’s ability to deliver fine‐grained, responsible sentiment insights that evolve with ‎new user language while safeguarding fairness across demographics‎.