A Decision Model for Ranking Asian Higher Education Institutes Using an NLP-Based Text Analysis Approach

Abstract

Identification of the best institute for higher education has become one of the most challenging issues in the present education system. It has become more complicated as more institutes exist with extraordinary infrastructural facilities. Therefore, a decision model is required to identify the best institute for higher education based on multiple criteria. This article proposes a Natural Language Processing (NLP) -based decision model for the identification of the best higher education institute using MCDM methods. The existing decision models for the selection of the best higher education institutions consider a limited number of criteria for decision-making. In this proposed model, 17 criteria and 15 institute datasets have been identified for the development of the decision model through extensive research and experts opinion. The NLP-based text analysis approach is applied to extract the relevant information and convert it to a suitable format. As the relative importance of the criteria plays a crucial role in decision-making, CRITIC and Rank centroid methods are applied for the calculation of relative weights of criteria. TOPSIS method is used to generate the ranking grades of alternatives for each criterion. An objective function is defined to calculate the evaluation scores and select the best institute for higher education. It has been observed that the ranks obtained from the developed model match pretty well with the ranks obtained from other MCDM methods and the experts.