Graph Neural Network Enhanced Sentiment Classification: Modeling Semantic Relationships in Large-Scale Text Data
Keywords:
sentiment classification, graph neural networks, semantic relationships, large-scale text data, system architecture, infrastructure, fairness, policyAbstract
Sentiment classification at scale has become a cornerstone of modern socio-technical infrastructures, powering applications in financial analytics, public health surveillance, and digital platform governance. While deep learning methods based on large pre-trained language models have advanced the state of the art, they often treat documents as independent samples, overlooking the rich web of semantic relationships that connect textual units across large corpora. Graph neural networks offer a compelling paradigm for explicitly encoding these interdependencies by constructing text-level graphs that capture lexical co-occurrence, discourse structure, and knowledge-grounded associations. This paper presents a systems-level analysis of integrating graph neural networks into sentiment classification pipelines deployed over large-scale text data. We examine architectural trade-offs between expressive graph construction, computational scalability, and operational robustness. Through detailed discussion of infrastructure design, we address distributed graph sampling, incremental graph updating, and the co-engineering of transformer-based encoders with graph message-passing layers. The work further explores governance and fairness implications arising when graph propagation amplifies biases encoded in the underlying relational structures. We assess sustainability dimensions, including the carbon footprint of graph-enhanced training regimes, and propose policy-aware strategies for auditing graph-augmented sentiment systems. By framing sentiment classification as a networked inference problem, the paper provides a comprehensive reference for building responsible, resilient, and interpretable large-scale sentiment analysis systems.
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