Personalized Sentiment Analysis for Recommendation Systems Based on User Preference Modeling
Keywords:
personalized sentiment analysis; recommendation systems; user preference modeling; deep learning; fairness; system architectureAbstract
Personalized sentiment analysis has emerged as a critical capability for modern recommendation systems that must interpret subjective user evaluations across heterogeneous contexts. This paper presents a system-level examination of personalized sentiment analysis grounded in user preference modeling. It argues that sentiment classification cannot be treated as a generic natural language processing component; rather, it must be designed as a preference-aware subsystem that reflects individual vocabulary, aspect weights, temporal dynamics, and contextual constraints. The discussion connects three research streams: recommendation systems, opinion mining, and deep learning architectures for text representation. A central emphasis is placed on structural trade-offs among accuracy, interpretability, latency, privacy, fairness, and maintainability. The paper examines architectural choices, including hybrid attention and contrastive learning mechanisms, joint modeling of review semantics and behavioral signals, and transformer-based personalization. It further addresses governance and infrastructure concerns such as data minimization, bias auditing, reproducible evaluation, and long-tail coverage. Through cross-domain illustrations and forward-looking analysis, the paper suggests that sustainable deployment requires not only improved classification performance but also institutional oversight, robust feedback loops, and system-level accountability. The conclusion outlines research directions in dynamic user modeling, cross-domain transfer, federated learning, and socio-technical governance. Overall, the work contributes a conceptual and architectural synthesis for integrating personalized sentiment analysis into recommendation platforms.
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