Sentiment Analysis of Online Reviews for Demand Forecasting and Personalized Pricing
Keywords:
sentiment analysis; online reviews; demand forecasting; personalized pricing; system architecture; algorithmic fairness; governanceAbstract
Online reviews have become a valuable but noisy source of market intelligence for firms seeking to improve demand forecasting and refine personalized pricing strategies. This article develops a system-level analysis of how sentiment analysis can be embedded into demand sensing and pricing decision infrastructures. It moves beyond algorithm-centric perspectives to examine structural trade-offs among data ingestion, sentiment extraction, forecasting integration, pricing mechanisms, governance, fairness, robustness, and long-term sustainability. The article argues that review-derived sentiment signals can enhance demand planning when they are treated as delayed behavioral sensors rather than direct measurements of willingness to pay. However, the use of such signals for personalized pricing introduces heightened risks related to opacity, discrimination, privacy, and consumer trust. The discussion integrates conceptual foundations from information systems, machine learning, marketing science, and technology policy. It emphasizes that sentiment-based demand forecasting and personalized pricing should be architecturally separated, with aggregate sentiment feeding operational decisions and individualized sentiment feeding only carefully governed pricing interventions. The paper further examines deployment challenges such as hidden technical debt, model drift, feedback loops, data supply chain fragility, and regulatory compliance. It concludes by identifying future research directions, including causal identification, interpretable hybrid models, consumer perception studies, and institutional mechanisms for algorithmic accountability. The interdisciplinary perspective contributes to the design of sentiment-driven market systems that are not only accurate and efficient but also robust, transparent, and socially legitimate.
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