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

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scientometrics and bibliometrics research Library Science and Information Computational and Text Analysis Methods

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.

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2013Data Science and its Relationship to Big Data and Data-Driven Decision Making 2020Research trends in text mining: Semantic network and main path analysis of selected journals 2013A co-word analysis of library and information science in China 2018A bibliometric analysis of text mining in medical research 2017Research methods: What's in the name?2020Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model 2018Bibliometrics, Scientometrics, Webometrics/ Cybermetrics, Informetrics and Altmetrics - An Emerging Field in Library and Information Science Research 2020The evolution of data science and big data research: A bibliometric analysis

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