Text Mining in Bibliometrics and Science Mapping: A Methodological Review

Abstract

ABSTRACT Text mining has become central to bibliometrics, providing quantitative insight into the semantic structure of scientific communication. This review surveys current methodological approaches to text‐based science mapping, including geometric embeddings, probabilistic models, network techniques, and neural embedding methods. The discussion examines how these approaches operate across different representations of text and evaluates their interpretability, stability, and statistical assumptions. Key issues include data quality, model validation, reproducibility, and the growing influence of large language models. Persistent challenges—language bias, topic instability, limited full‐text access, and model opacity—raise open questions about dynamic, multimodal, and ethically grounded science mapping.

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Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.