Sentiment-Driven Product Demand Forecasting from Consumer-Generated Online Content
Keywords:
sentiment analysis; demand forecasting; consumer-generated content; text mining; system architecture; data governance; algorithmic fairness; operational robustnessAbstract
Consumer-generated online content has become a rich but unstable source of information for anticipating product demand. This paper examines sentiment-driven demand forecasting as a large-scale socio-technical system rather than as a narrow modeling problem. It discusses the transformation of unstructured text into forecasting signals and analyzes the architectural layers required for ingestion, sentiment extraction, demand modeling, and operational decision making. The paper evaluates structural trade-offs among recurrent, convolutional, attention-based, and contrastive learning approaches while emphasizing that model accuracy alone is insufficient for deployment. It explores data infrastructure requirements, latency, retraining, failure modes, and sustainability constraints. The analysis further addresses fairness, robustness, and governance challenges that arise when forecasts influence inventory, pricing, and marketing decisions. Drawing on prior empirical work in marketing, econometrics, natural language processing, and platform governance, the paper articulates how sentiment signals interact with market structure, platform dynamics, and consumer behavior. It concludes by outlining policy implications and future research directions, including causal identification, multimodal content, synthetic text, and responsible deployment. The central argument is that sentiment-driven demand forecasting must be designed as an accountable, adaptive, and resilient system in which technical components, human oversight, and institutional controls are treated as equally important.
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