  In the contemporary world, with the incubation of advanced technologies and
tremendous outbursts of research works, analyzing big data to incorporate
research strategies becomes more helpful using the tools and techniques
presented in the current research scenario. This paper indeed tries to tackle
the most prominent challenges relating to big data analysis by utilizing a text
mining approach to analyze research data published in the field of production
management as a case to begin with. The study has been conducted by considering
research data of International Journal of Production Research (IJPR) indexed in
Scopus between 1961-2017 by dividing the analysis incurred into 3 fragments
being 1961-1990, 1991-2010 and finally 2011-2017 as a case to highlight the
focus of journal. This has indeed provided multi-faceted benefits such as
increasing the effectiveness of the procured data with well-established
comparisons between R and Python Programming along with providing detailed
research trends on the research work incubated. The results of the study
highlighted some most prominent topics in the existing IJPR literature such as
system's optimization, supplier selection, process design, etc. providing
well-established details relating to ongoing research works. The study also
compared both languages suiting to a particular field of study for better
comprehension and vastness of the research topics. The current research work is
one of the part of a copyright work with registration number SW-10310/2018
titled Program for Analyzing Key Trends in Research Data-set. It has been
designed in Python for carrying out detailed content analysis based on the
available research database in bib format as in the current context it has been
applied for IJPR journal and can be replicated on articles of any domain found
using keyword search.
