Deep Reinforcement Learning for Ethically‐Aware Personalized Sentiment Analysis

Sentiment analysis has become an essential tool for extracting opinions from user-generated text; however, conventional models often treat all users uniformly and focus solely on predictive accuracy, neglecting ethical fairness and individual linguistic variation. To address these limitations, this research presents a comprehensive ethically-aware and personalized sentiment analysis framework grounded in deep reinforcement learning and attention mechanisms. The proposed framework introduces multiple variants of an Attention-Driven Reinforcement Sentiment Analyzer (ADRSA), namely ADRSA with Aspect-Based Polarity Analysis (APA), Aspect-Based Fairness Representation (AFR), and Aspect-Based Personalization Representation (APR). These models leverage attention to identify sentiment-bearing words, while reinforcement learning optimizes sentiment decisions through reward functions that explicitly incorporate accuracy, ethical fairness, bias control, and personalization. To provide meaningful benchmarks, the framework is evaluated against deep learning baselines including LSTM with APA, AFR, and APR, a Recurrent Neural Memory System with AFR (RNMS-AFR), and a classical Support Vector Machine with APA (SVM-APA). Aspect-level sentiment analysis is employed to capture fine-grained opinions across multiple aspects within a single text. Experimental results demonstrate that ADRSA-based models consistently outperform traditional deep learning and machine learning baselines in terms of accuracy, F1-score, fairness consistency, and personalized sentiment interpretation. The findings confirm that integrating reinforcement learning with attention, ethical constraints, and personalization enables context-aware, bias-controlled, and trustworthy sentiment analysis. This work establishes a robust foundation for responsible, user-centric sentiment analysis systems applicable to real-world social and commercial environments.