Temporal Sentiment Modeling for Tracking Public Opinion Dynamics During Emerging Events
Keywords:
temporal sentiment analysis; public opinion dynamics; computational social science; natural language processing; event monitoring; system governance; fairnessAbstract
Public opinion during emerging events evolves through dynamic, contested, and time-sensitive discourse across digital platforms. Capturing this evolution requires more than static polarity classification; it demands temporal sentiment modeling that integrates natural language processing, time-series reasoning, and socio-technical infrastructure. This paper presents a system-level examination of temporal sentiment modeling for tracking public opinion dynamics during crises, political transitions, public health emergencies, and other rapidly unfolding events. It argues that effective deployment depends less on isolated algorithmic gains than on architectural choices, data governance, robustness under distributional drift, interpretability, fairness, and sustainable operational integration. The discussion reviews conceptual foundations from sentiment analysis and computational social science, then develops a layered architecture that connects ingestion, normalization, event segmentation, representation learning, temporal aggregation, and decision support. Structural trade-offs are examined across lexicon-based, neural, and hybrid approaches, with attention to evolving context and label scarcity. The paper further addresses infrastructure requirements, including provenance, privacy, latency, and cross-platform alignment. Robustness concerns such as concept drift, adversarial manipulation, and rumour propagation are situated within broader governance and policy frameworks. Fairness, transparency, and accountability are treated as system properties rather than post hoc constraints. The paper concludes with future directions for resilient, interpretable, and ethically governed temporal opinion monitoring systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
