Deep Learning-Based Opinion Mining for Brand Reputation Management in Social Media
Keywords:
deep learning; opinion mining; brand reputation; social media; governance; fairnessAbstract
Brand reputation management increasingly depends on the systematic analysis of user-generated content across social media platforms. Opinion mining, also referred to as sentiment analysis, provides a computational foundation for extracting evaluative signals from unstructured text at scale. Deep learning architectures have substantially improved the accuracy and granularity of opinion mining systems, enabling organizations to monitor brand perception in near real time. However, the design and deployment of such systems involve structural trade-offs that extend beyond model accuracy. This paper presents a system-level examination of deep learning-based opinion mining for brand reputation management. It addresses the evolution of computational opinion analysis, the architectural foundations of deep neural models, data infrastructure requirements, fine-grained opinion extraction, deployment integration, governance, robustness, fairness, sustainability, and policy implications. The discussion emphasizes that effective reputation management systems must balance predictive performance with data quality, interpretability, organizational oversight, regulatory compliance, and long-term operational resilience. The paper further considers how hybrid attention mechanisms and contrastive learning strategies can support more stable sentiment classification under noisy social media conditions. It argues that future systems should be designed not only as technical pipelines but also as socio-technical infrastructures that align organizational decision-making with ethical principles and public accountability. By integrating computational and governance perspectives, this study offers a framework for researchers and practitioners seeking to build responsible and scalable opinion mining systems for brand reputation management.
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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.
