Optimizing academic literature review using Textmining in R: An automated approach

Abstract

Literature review is crucial for research development, but the growing number of digital publications has complicated this process, increasing the risk of bias. This article aims to develop and apply an automated academic literature review approach using text mining techniques in the R programming language. A database of 86,820 articles published in scientific journals, containing the search terms ("model" AND "growth" AND "economic"), hosted in the open access database Redalyc, was retrieved. A programming syntax was developed that optimized data download, processing and analysis, allowing its replication in future literature review processes. This study demonstrates the potential of text mining tools and automated bibliometric analysis, using R, to optimize literature review in the scientific field.