Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose
This addresses the problem for prospective applicants by providing a tool to evaluate SOPs, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the unpredictability of Computer Science graduate school admissions by developing a machine learning system to predict acceptance based on Statement of Purpose (SOP) content, achieving 92% accuracy with an SVM classifier on 50 manually verified SOPs.
We present a quantitative, data-driven machine learning approach to mitigate the problem of unpredictability of Computer Science Graduate School Admissions. In this paper, we discuss the possibility of a system which may help prospective applicants evaluate their Statement of Purpose (SOP) based on our system output. We, then, identify feature sets which can be used to train a predictive model. We train a model over fifty manually verified SOPs for which it uses an SVM classifier and achieves the highest accuracy of 92% with 10-fold cross-validation. We also perform experiments to establish that Word Embedding based features and Document Similarity-based features outperform other identified feature combinations. We plan to deploy our application as a web service and release it as a FOSS service.