Explainable Artificial Intelligence for Social Media Sentiment Mining: Integrating Transformer-Based Attention and Human-Centric Interpretation
Keywords:
explainable artificial intelligence, sentiment mining, social media, transformer attention, human-centric interpretation, algorithmic fairness, socio-technical systemsAbstract
The proliferation of user-generated content on social media platforms has created enormous demand for automated sentiment mining tools that can accurately capture nuanced opinion, emotion, and stance at scale. While transformer-based deep learning architectures have achieved state-of-the-art performance in text sentiment classification, their inherent opacity presents a critical barrier to adoption in high-stakes domains such as public health surveillance, financial market analysis, and political discourse monitoring. This paper presents a comprehensive interdisciplinary examination of explainable artificial intelligence (XAI) approaches tailored to social media sentiment mining, focusing on the integration of transformer-based attention mechanisms with human-centric interpretation frameworks. We survey contemporary architectural paradigms including bidirectional encoder representations, generative pre-trained transformers, and their fine-tuning strategies, then systematically analyze how intrinsic attention weights can be repurposed to generate post hoc explanations. The discussion extends to the structural tension between explanation fidelity and model complexity, the infrastructure requirements for deploying interpretable models in real-time social media pipelines, and the governance frameworks necessary to ensure fairness and accountability. By examining cross-domain case illustrations from healthcare misinformation detection, crisis response coordination, and consumer sentiment tracking, we identify key trade-offs in latency, transparency, and robustness. The paper further addresses sustainability concerns related to the computational carbon footprint of large-scale transformer fine-tuning and inference, proposing lightweight distillation and modular explanation proxies as viable paths forward. Policy implications concerning algorithmic transparency mandates and auditability standards are synthesized with technical design choices, culminating in a set of architectural principles for building socially responsible sentiment mining systems. The analysis reveals that effective explainability cannot be reduced to a single technical method but must be constructed as a socio-technical assemblage where attention visualization, contrastive example generation, and structured natural language rationales are combined under participatory design with domain stakeholders.
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