Analyzing the Role of Natural Language Processing in Detecting Public Sentiment Shifts on Social Media
Keywords:
natural language processing, sentiment analysis, social media, public opinion, system architecture, governance, fairness, policy, robustnessAbstract
The rapid expansion of social media platforms has created an unprecedented volume of user-generated textual data that reflects evolving public opinion on political, social, and economic issues. Natural language processing has emerged as a critical technological lever for detecting sentiment shifts at scale, enabling real-time analysis of collective emotional and attitudinal changes. This paper provides a systematic examination of the role of natural language processing in such detection tasks, moving beyond algorithmic performance to consider the broader system-level implications of deploying sentiment analysis infrastructures. The discussion addresses structural trade-offs between accuracy and interpretability, the architectural choices that underpin scalable processing pipelines, and the governance challenges that arise when these systems inform policy decisions or public discourse. Emphasis is placed on the sustainability of deployed models, the robustness of sentiment signals against adversarial manipulation and data drift, and the fairness considerations inherent in training data that often reflects systemic biases. Cross-domain comparisons illustrate how sentiment detection approaches differ across crisis communication, political polling, and market analysis. The paper further explores the ethical boundaries of automated sentiment monitoring and the policy frameworks needed to ensure responsible use. By integrating insights from computational linguistics, social science, and infrastructure design, this work offers a comprehensive perspective on the promises and perils of using natural language processing to track societal sentiment.
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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.
