Context-Based Understanding of Scholarly Articles and Stem Research Using Text Mining Approach

Abstract

Publishers adopt different ways to sort the papers. Two of the most common tagging methods are Journal based tagging and Content-based tagging. In journal based tagging the tags are given to the papers depending on the published journal's interested fields. On the other hand, authors like Dimension use machine learning to classify and sort the paper's categories. Which method conveys more information and accuracy is a question to be answered. This study revealed that content base tagging is better than journal base tagging for sorting the paper’s categorization by using title, abstract, and keywords. Gender discrimination or inequality refers to the unequal treatment of humans depending on their gender. To measure gender discrimination Gaye et al. used 3 statistical dimensions labor market, empowerment, and reproductive health. Education is one of the most important indicators of the gender discrimination index. Stereotype thinking plays a key role to demotivate females to participate in technical fields. World Bank data shows that the girl's participation ratio in primary and secondary education is increasing in developing countries like India, Bangladesh, Pakistan, Indonesia, and Nepal. On the other hand, gender bias has a negative impact on girls' education and choosing STEM-related subjects. Technical Education or a full form of STEM (STEM) related education plays a vital role in the development of a nation. This research aims to find out the veracity of stereotypical views and thinking. Besides, this research revealed female performance on technical and nontechnical subjects and find that females are better than males in both technical and nontechnical subjects.