Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 14, August 2022

Bookshelf – A Document Categorization for Library using Text Mining


Carlo Petalver, Roderick Bandalan and Gregg Victor Gabison, University of San Jose, Philippines


Categorizing books and other archaic paper sources to a course reference or syllabus is a challenge in library science. The traditional way of categorization is manually done by professionals and the process of seeking and retrieving information can be frustrating. It needs intellectual tasks and conceptual analysis of a human effort to recognize similarities of items in determining the subject to the correct category. Unlike the traditional categorization process, the author implemented the concept of automatic document categorization for libraries using text mining. The project involves the creation of a web app and mobile app. This can be accomplished through the use of a supervised machine learning classification model using the Support Vector Machine algorithm that can predict the given category of data from the book or other archaic paper sources to the course syllabus they belong to.


Text Mining, Document Categorization, Classification algorithm, Support Vector Machine, Library.