Topic Analysis and Mapping of Tuberculosis Research Using Text Mining and Co-Word Analysis

Abstract

Tuberculosis is still one of the most severe progressive diseases; it severely limits the social and economic development of many countries. In the present study, the topic trend of scientific publications on tuberculosis has been examined using text mining techniques and co-word analysis with an analytical approach. The statistical population of the study is all global publications related to tuberculosis. In order to extract the data, the Scopus citation database was used for the period 1900 to 2022. The main keywords for the search strategy were chosen through consultation with thematic specialists and using MESH. Python programming language and VOSviewer software were applied to analyze data. The results showed four main topics as follows: “Clinical symptoms” (41.8%), “Diagnosis and treatment” (28.1%), “Bacterial structure, pathogenicity and genetics” (22.3%), and “Prevention” (7.84%). The results of this study can be helpful in the decision of this organization and knowledge of the process of studies on tuberculosis and investment and development of programs and guidelines against this disease.