Modeling document labels using Latent Dirichlet allocation for archived documents in Integrated Quality Assurance System (IQAS)

Abstract

Background As part of the transition of every higher education institution into an intelligent campus here in the Philippines, the Commission of Higher Education has launched a program for the development of smart campuses for state universities and colleges to improve operational efficiency in the country. With regards to the commitment of Camarines Sur Polytechnic Colleges to improve the accreditation operation and to resolve the evident problems in the accreditation process, the researchers propose this study as part of an Integrated Quality Assurance System that aims to develop an intelligent model that will be used in categorizing and automating tagging of archived documents used during accreditation. Methods As a guide in modeling the study, the researchers use an agile method as it promotes flexibility, speed, and, most importantly, continuous improvement in developing, testing, documenting, and even after delivery of the software. This method helped the researchers design the prototype with the implementation of the said model to aid the file searching process and label tagging. Moreover, a computational analysis is also included to understand the result from the devised model further. Results As a result, from the processed sample corpus, the document labels are faculty, activities, library, research, and materials. The labels generated are based on the total relative frequencies, which are 0.009884, 0.008825, 0.007413, 0.007413, and 0.006354, respectively, that have been computed between the ratio of how many times the term was used in the document and the total word count of the whole document. Conclusions The devised model and prototype support the organization in file storing and categorization of accreditation documents. Through this, retrieving and classifying the data is easier, which is the main problem for the task group. Further, other clustering, modeling, and text classification patterns can be integrated into the prototype.