Modeling the Influence of News Sentiment on Public Perception Using Natural Language Processing
Abstract
The rapid digitization of information ecosystems has fundamentally altered the feedback loops between journalistic output and public sentiment. This research examines the complex socio-technical dynamics of news sentiment and its quantifiable impact on public perception through the lens of large-scale natural language processing. By treating the news media landscape as a vast, interconnected information infrastructure, this paper explores the structural trade-offs involved in deploying automated sentiment analysis to monitor societal trends. We analyze the architectural requirements for robust, fair, and scalable linguistic models capable of processing heterogeneous data streams in real-time. Central to this inquiry is the tension between computational efficiency and the preservation of nuanced sociopolitical context. The discussion extends beyond mere algorithmic performance to encompass the broader implications for governance, democratic robustness, and the sustainability of digital public spheres. We argue that the systematic modeling of news sentiment necessitates a multidisciplinary framework that integrates technical precision with policy-aware deployment strategies. By evaluating the trade-offs between centralized and decentralized sentiment monitoring systems, the paper provides a roadmap for the ethical integration of artificial intelligence into public opinion research. Ultimately, this work highlights the critical need for transparent algorithmic governance to mitigate the risks of perception manipulation while harnessing the potential of natural language processing to enhance social cohesion and informed policy-making.
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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.
