A text mining and topic modeling based bibliometric exploration of information science research

Tipawan Silwattananusarn, Pachisa Kulkanjanapiban


This study investigates the evolution of information science research based on bibliometric analysis and semantic mining. The study further discusses the value and application of metadata tagging and topic modeling. 42738 articles were extracted from Clarivate Analytic's Web of Science Core Collection for the period 2010-20. This study was divided into two phases. Firstly, bibliometric analyzes were performed with VOSviewer, Secondly, the topic identification and evolution trends of information science research were conducted through the LDA topic model using Topic-Modeling-Toolkit (TMT). The top 10 core topics (tags) were found to be information research design, information health-based, model data public, study information studies, analysis effect implications, knowledge support web, data research, social research study, study media information, and research impact time for the studied period. Not only does topic modeling assist in identifying popular topics or related areas within a researcher's area, but it may be used to discover emerging topics or areas of study throughout time.


Text Mining; Latent Dirichlet Allocation; Topic Modeling; Bibliometrics; Research Trends;

DOI: http://doi.org/10.11591/ijai.v11.i3.pp%25p


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