Data mining topics in the discipline of library and information science: analysis of influential terms and Dirichlet multinomial regression topic model

Sukjin You, Soohyung Joo, Marie Katsurai

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications. Design/methodology/approach: A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling. Findings: The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade. Originality/value: This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Original languageEnglish
Pages (from-to)65-85
Number of pages21
JournalAslib Journal of Information Management
Volume76
Issue number1
DOIs
StatePublished - Jan 2 2024

Bibliographical note

Publisher Copyright:
© 2022, Emerald Publishing Limited.

Keywords

  • Bibliographic records
  • Data mining
  • Library and information science
  • Research topics
  • Textual analysis
  • Trend analysis

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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