Knowledge Economy Studies

A Bibliometric Analysis of Text Mining Applications in Knowledge Management

Document Type : Original Article

Authors

Fatemeh Abbasi 1
Zahra Khojasteh 2
Ameneh Khadivar 3

1 Assistant Professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

2 Master of Management of Information Technology, Alzahra University, Tehran, Iran Email: khojasteh993@gmail.com

3 Corresponding Author, Associate Professor, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran Email: a.khadivar@alzahra.ac.ir

https://doi.org/10.22034/kes.2025.2064288.1065
Abstract
Despite the growing importance of text mining in knowledge management, a comprehensive analysis of its evolution, key contributors, and emerging trends has remained limited. This study addresses this gap by conducting a bibliometric analysis of the field from 2003 to 2023, focusing on long-term trends, influential actors, and thematic shifts. To this end, a scientometric analysis was conducted using data sourced from the Web of Science database. By applying filters for publication year, language, and document type, 590 documents were selected for analysis. Co-occurrence and co-authorship analyses were performed using VOSviewer to visualize the scholarly contributions and thematic developments. The study revealed notable publication growth, particularly after 2019. 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 (2015) paper entitled "Transfer Learning Using Computational Intelligence: A Survey" emerged as the most cited work. Key areas of focus included natural language processing, information extraction, and deep learning, demonstrating the increasing influence of technological innovations on the field. 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

Subjects

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Knowledge Economy Studies
Volume 2, Issue 2
October 2025
Pages 155-174

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