A Bibliometric Analysis of Text Mining Applications in Knowledge Management

AbstractPurpose: Despite the growing importance of text mining in knowledge management, a comprehensive analysis of its evolution, key contributors, and emerging trends remains limited. This study addresses this gap by conducting a bibliometric analysis of the field from ۲۰۰۳ to ۲۰۲۳, focusing on long-term trends, influential actors, and thematic shifts.Design/methodology/approach: A scientometric analysis was conducted using data sourced from the Web of Science database. By applying filters for publication year, language, and document type, ۵۹۰ documents were selected for analysis. Co-occurrence and co-authorship analyses were performed using VOSviewer to visualize scholarly contributions and thematic developments.Findings: The study reveals notable publication growth, particularly after ۲۰۱۹. Prominent authors such as Rafael Valencia-Garcia and Francisco Garcia-Sanchez, along with leading institutions like the Chinese Academy of Sciences and Tsinghua University, were identified as major, contributors. China stood out as the leading country in terms of publication numbers and citation impact. Ji Luo’s ۲۰۱۵ paper, "Transfer Learning Using Computational Intelligence: A Survey," emerged as the most cited work. Key areas of focus include natural language processing, information extraction, and deep learning, demonstrating the increasing influence of technological innovations on the field.Originality: This work provides a detailed bibliometric overview of text mining applications in knowledge management, highlighting significant trends, leading researchers, and core topics. It offers actionable insights for scholars and practitioners to navigate and contribute to this evolving area of study.Keywords: Bibliometrics, Co-authorship Analysis, Co-occurrence Analysis, Knowledge Management, Text Mining.

کلیدواژه ها:

Bibliometrics ، Co-authorship Analysis ، Co-Occurrence Analysis ، Knowledge Management ، text mining