A Study on Applicability of Machine Learning for Book Classification of Public Libraries: Focusing on Social Science and Arts

Kwak, C. W. (2021). A Study on Applicability of Machine Learning for Book Classification of Public Libraries: Focusing on Social Science and Arts. 한국비블리아학회지, 32(1), 133-150, https://doi.org/10.14699/kbiblia.2021.32.1.133

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Abstract

The purpose of this study is to identify the applicability of machine learning targeting titles in the classification of books in public libraries. Data analysis was performed using Python’s scikit-learn library through the Jupiter notebook of the Anaconda platform. KoNLPy analyzer and Okt class were used for Hangul morpheme analysis. The units of analysis were 2,000 title fields and KDC classification class numbers (300 and 600) extracted from the KORMARC records of public libraries. As a result of analyzing the data using six machine learning models, it showed a possibility of applying machine learning to book classification. Among the models used, the neural network model has the highest accuracy of title classification. The study suggested the need for improving the accuracy of title classification, the need for research on book titles, tokenization of titles, and stop words.

keywords: 머신러닝, 표제 분류, 도서관 분류, 파이썬, 사이킷런 라이브러리, Machine Learning, Title Classification, Library Classification, Python, Scikit-learn Library

Submission Date: 2021-02-22

Revised Date: 2021-03-01

Accepted Date: 2021-03-19

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Article Contents

Vol.32 No.1

A Study on Applicability of Machine Learning for Book Classification of Public Libraries: Focusing on Social Science and Arts

Abstract

한국비블리아학회지