A Topic Modeling Analysis of Circular Economy and Big Data Research Using BERTopic and SciBERT

Abstract

This study presents a hybrid topic modeling framework to map emerging themes in Circular Economy (CE)–Big Data literature. Using a corpus of 1,171 articles (2015–2025), three topic modeling techniques like BERTopic with SciBERT embeddings, Latent Dirichlet Allocation (LDA), and Top2Vec were applied and evaluated using coherence and diversity metrics. The transformer-based BERTopic–SciBERT model yielded 88 fine-grained topics with high coherence (mean Cᵥ= 0.47) and diversity (0.72), outperforming classical models in semantic quality and topic distinctness. Extracted topics were organized into five ontology-based domains: technical enablers, operational practices, policy/social, business models, and miscellaneous. Community detection in topic-similarity networks revealed distinct research clusters that moderately aligned with these ontology domains. Temporal analysis showed a structural shift after 2019, with increased focus on digitalization and data-driven sustainability. Policy-related themes remained limited, indicating gaps in governance research. Model robustness was validated through dimensionality sensitivity and embedding ablation, confirming stability and interpretability. A Sankey diagram was developed to visualize topic–domain–community linkages. The proposed framework provides a replicable method for semantic mapping in interdisciplinary sustainability research and supports strategic insight into evolving research directions in the CE–BD field.