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In the contemporary world, with the incubation of advanced technologies andtremendous outbursts of research works, analyzing big data to incorporateresearch strategies becomes more helpful using the tools and techniquespresented in the current research scenario. This paper indeed tries to tacklethe most prominent challenges relating to big data analysis by utilizing a textmining approach to analyze research data published in the field of productionmanagement as a case to begin with. The study has been conducted by consideringresearch data of International Journal of Production Research (IJPR) indexed inScopus between 1961-2017 by dividing the analysis incurred into 3 fragmentsbeing 1961-1990, 1991-2010 and finally 2011-2017 as a case to highlight thefocus of journal. This has indeed provided multi-faceted benefits such asincreasing the effectiveness of the procured data with well-establishedcomparisons between R and Python Programming along with providing detailedresearch trends on the research work incubated. The results of the studyhighlighted some most prominent topics in the existing IJPR literature such assystem's optimization, supplier selection, process design, etc. providingwell-established details relating to ongoing research works. The study alsocompared both languages suiting to a particular field of study for bettercomprehension and vastness of the research topics. The current research work isone of the part of a copyright work with registration number SW-10310/2018titled Program for Analyzing Key Trends in Research Data-set. It has beendesigned in Python for carrying out detailed content analysis based on theavailable research database in bib format as in the current context it has beenapplied for IJPR journal and can be replicated on articles of any domain foundusing keyword search.

Python vs. R: A Text Mining Approach for analyzing the Research Trends in Scopus Database