Abstract
The goal of this paper is to provide a bibliometric analysis of scientific publications that employ text mining in management. To accomplish this, the authors collected 1282 documents from the Web of Science and performed performance analysis and science mapping with the help of the Bibliometrix package in Rstudio. The performance analysis used a range of bibliometric indicators such as productivity, citations, h-index, and m-quotient, in order to identify research trends and the most influential journals, authors, countries, and literature in the study. Science mapping used author keywords co-occurrence, co-authorship, and co-citation analysis to reflect the conceptual, social, and intellectual structure of the research. Specifically, we have seen an exponential increase in the use of text mining in management in recent years. The United States is the dominant country for research, having the earliest studies and the highest number of literature and citations. Furthermore, the research themes showed that topic modeling is at the forefront of current text mining research about management. This study will help scholars and management practitioners interested in the intersection of text mining and management to quickly understand the latest advances in research.
Highlights
Data types can be structured, semi-structured, or even heterogeneous. e method of discovering knowledge can be mathematical, nonmathematical, or inductive. e discovered knowledge can be used for information management, query optimization, decision support, and data maintenance
In contrast to generalized data mining, which deals with structured data, text mining focuses on the analysis and modeling of unstructured natural language text, such as online news, scientific research papers, and medical documents. erefore, it is a comprehensive technology that exploits natural language processing, pattern classification, machine learning, statistics, and other techniques [2, 3]
We identified 11 different keywords similar or related to text mining, which are commonly used in the field of management
Summary
Data types can be structured, semi-structured, or even heterogeneous. e method of discovering knowledge can be mathematical, nonmathematical, or inductive. e discovered knowledge can be used for information management, query optimization, decision support, and data maintenance. Until the 1990s, it was combined with many newly developed techniques to produce a wider range of analytical tasks, including document classification, clustering, document meaning extraction, association mining, trend analysis, and machine translation [7], which provides a variety of methods for discovering knowledge and patterns from massive amounts of unstructured text data. There are limited bibliometric studies in the academic community on the application of text mining techniques in different subject areas, and they are mainly focused on the field of biomedicine [12,13,14], and to our knowledge, there are no bibliometric studies on the application of text mining in management For this reason, this paper collects relevant literature from the WOS database and performs bibliometrics to address the following research questions: RQ1. What are the structure characteristics of text mining literature about management?
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