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

  • Journal of the Korean Biblia Society for Library and Information Science
  • 2021, 32(1), pp.133-150
  • DOI : 10.14699/kbiblia.2021.32.1.133
  • Publisher : Journal Of The Korean Biblia Society For Library And Information Science
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : February 22, 2021
  • Accepted : March 19, 2021
  • Published : March 30, 2021

Chul Wan Kwak 1

1강남대학교

Accredited

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.

Citation status

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