When text mining meets science mapping in the bibliometric analysis: A review and future opportunities

Abstract

The increasing volume of scientific publications has created a need for more efficient and effective literature review processes. Bibliometric analysis is a quantitative approach to analysing bibliographic data extracted from research studies to identify publishing patterns and trends within specific knowledge domains. Science mapping is a widespread technique in bibliometric analysis that enables researchers to reveal the structure of their respective fields and identify dominant themes. However, there is still a lack of clarity and transparency in describing the science mapping process, which can hinder continued refinement and improvement of this critical field of research. This study provides a comprehensive overview of science mapping in bibliometric analysis based on published review studies from prestigious international journals. It outlines the science mapping mechanism and explores challenges and opportunities, focusing on incorporating text mining approaches to support the analytical literature review process. The study sheds light on previously unexplored mechanisms in the literature of bibliometric analysis, revealing gaps in existing research. The study contributes to the growing body of research on bibliometric analysis by highlighting the need for continuous improvement and the deployment of text mining techniques to support the analysis of scientific publication data. This study offers valuable insights for researchers, policymakers, and practitioners seeking to enhance their understanding of science mapping and bibliometric analysis.