Using Computational Linguistics to Analyse Main Research Directions in Economy of Regions
DOI:
https://doi.org/10.17059/ekon.reg.2022-2-3Keywords:
topic modelling, machine learning, computational linguistics, text mining, literature review, academic journal, spatial economics, environmental economics, scientometrics, third-party fundingAbstract
Over the past decades, the process of knowledge generation has accelerated, producing a lot of scientific publications, which makes reviewing even a relatively narrow subject area very demanding, if not impossible. However, recent text data mining tools can assist researchers in conducting such analysis in an objective and time-efficient way. We conduct such a literature review on 1307 articles published in the journal Economy of Regions from 2010 to 2021 using advanced topic modelling techniques. This analysis aims to describe the main research areas in the journal over time, the dynamics of their popularity and the relationship with key quantitative indicators. We identified 22 topics ranging from “Agriculture” and “Economic Geography” to “Fiscal Policy” and “Entrepreneurship”. We estimate how popularity of these topics was changing over time and find topics that gained the most popularity from 2010 to 2021 (+17.61 %, “Spatial Economics”) or lost it (-14.58 %, “Economics of Innovation”). The topic of environmental economics collects the largest number of citations per article (3.64, on average), and the topics on monetary policy and poverty are the most popular among manuscripts in English, which is also true for articles written by authors with foreign affiliation. Papers with third-party funding are concentrated the most in “Spatial Economics” (around 11 %), and the least — in “Agriculture”. Our results can help to understand the evolution in scope of research of Economy of Regions and serve researchers to find promising directions for future studies.
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