A SYSTEMATIC LITERATURE REVIEW ON BIBLIOMETRIC PROFILING ANALYSIS USING MACHINE LEARNING

In the constantly expanding area of scholarly inquiry, the need for efficient instruments or tools to measure and evaluate the effectiveness of literary works cannot be overestimated. As a field of study, bibliometrics is truly informative as it measures research output in a given field, further assisting in the areas of research grants, publications and evidence for decision-making and future planning in the education sector. It is a systematic way of evaluating productivity. Hence, this research attempts to carry out a literature review regarding the application of Bibliometric Profiling Analysis in the context of Machine Learning (ML). This research involved the assessment and evaluation of the research grants, innovations, published articles, journals and proceeding papers. All materials were retrieved using online databases such as IEEE Xplore, Ingenta Connect, Sage Journal, Science Direct and Proquest. To achieve this, technologies including data mining, network analysis and natural language processing techniques were applied. From the review, the Support Vector Machine (SVM) is a machine learning algorithm frequently used in Bibliometric Profiling Analysis.