Metadata Tagging and Prediction Modeling: Case Study of DJLIT (2008–17)

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Authors: Manika Lamba and Margam Madhusudhan

Journal: World Digital Libraries

Volume: 12

Issue: 1

DOI: 10.18329/09757597/2019/12103

Abstract: The present paper describes the importance and usage of metadata tagging and prediction modeling tools for researchers and librarians. 387 articles were downloaded from DESIDOC Journal of Library and Information Technology (DJLIT) for the period 2008-17 excluding guest editorials and special editions. This study was divided into two phases. The first phase determined the core Topics from the research articles using Topic-Modeling-Tool (TMT) , which was based on latent Dirichlet allocation (LDA), whereas the second phase employed prediction analysis using RapidMiner toolbox to annotate the future research articles on the basis of the modeled topics. The core topics (tags) were found to be digital libraries, information literacy, scientometrics, open access, and library resources for the studied period. This study further annotated the scientific articles according to the modeled topics to provide a better searching experience to its users. Sugimoto, Li, Russell, et al. (2011), Figuerola, Marco, and Pinto (2017), and Lamba and Madhusudhan (2018) have performed studies similar to the present paper but with major modifications.

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