Automatic Classification of research grants proposals using a multi– class machine learning model

Abstract

Research and Development has become fundamental to the economic development of every nation. Many countries have established institutions to promote, support and fund research and innovation in societies. These institutions seek to monitor and keep track of how much money they are willing to or have been spending on research and development activities in specific fields or topics. Funding investment decisions are based on whether proposed research ideas fall under disciplines of interest to national development. It is therefore imperative that research proposal documents submitted for funding consideration are classified according to respective disciplines. This paper explores the adaptation of the text classifier Support Vector Machine (SVM) for multi-classification and use it to automatically classify scholarly research documents and predict eligibility of funding. The experiment results demonstrate that the SVM model performed well with an accuracy performance of 89%. The study recommends implementing Application Programming Interface (API) endpoints for the model, to facilitate its integration with third-party tools and services to automatically classify the research proposal documents and award research grants.