World Digital Libraries 12(1): 33-89 (2019) DOI: 10.18329/09757597/2019/ 12103 Metadata Tagging and Prediction Modeling: Case Study of DESIDOC Journal of Library and Information Technology (2008-17) M. Lamba Research Scholar, Department of Library and Information Science, University of Delhi, Delhi-110007 (E): lambamanika07@gmail.com M. Madhusudhan Associate Professor and former Deputy Dean (Academics), Department of Library and Information Science, University of Delhi, Delhi (E): mmadhusudhan@libinfosci.du.ac.in  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 Journalof Library and Information Technology (DJLIT) for the period 2008-17. This study was divided into two phases. The first phase determined the core topics from theresearcharticles using Topic-Modeling- Toolkit (TMT), which wasbased 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 resourcesfor the studied period. This study further annotated thescientific articles according to the modeledtopicsto provide a better searching experiencetoits 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. Keywords: Metadata tagging, DESIDOC Journal of Library and Information Technology(DJLIT), Latent Dirichlet allocation (LDA), Information retrieval, Naive Bayes, Prediction modeling, Support Vector Machine (SVM), Text mining, Topic modeling World Digital Libraries 12(1): 33-89 (2019) DOI: 10.18329/09757597/2019/ 12103 Metadata Tagging and Prediction Modeling: Case Study of DESIDOC Journal of Library and Information Technology (2008-17) M. Lamba Research Scholar, Department of Library and Information Science, University of Delhi, Delhi-110007 (E): lambamanika07@gmail.com M. Madhusudhan Associate Professor and former Deputy Dean (Academics), Department of Library and Information Science, University of Delhi, Delhi (E): mmadhusudhan@libinfosci.du.ac.in  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 Journalof Library and Information Technology (DJLIT) for the period 2008-17. This study was divided into two phases. The first phase determined the core topics from theresearcharticles using Topic-Modeling- Toolkit (TMT), which wasbased 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 resourcesfor the studied period. This study further annotated thescientific articles according to the modeledtopicsto provide a better searching experiencetoits 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. Keywords: Metadata tagging, DESIDOC Journal of Library and Information Technology(DJLIT), Latent Dirichlet allocation (LDA), Information retrieval, Naive Bayes, Prediction modeling, Support Vector Machine (SVM), Text mining, Topic modeling World Digital Libraries 12(1): 33-89 (2019) DOI: 10.18329/09757597/2019/ 12103 Metadata Tagging and Prediction Modeling: Case Study of DESIDOC Journal of Library and Information Technology (2008-17) M. Lamba Research Scholar, Department of Library and Information Science, University of Delhi, Delhi-110007 (E): lambamanika07@gmail.com M. Madhusudhan Associate Professor and former Deputy Dean (Academics), Department of Library and Information Science, University of Delhi, Delhi (E): mmadhusudhan@libinfosci.du.ac.in  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 Journalof Library and Information Technology (DJLIT) for the period 2008-17. This study was divided into two phases. The first phase determined the core topics from theresearcharticles using Topic-Modeling- Toolkit (TMT), which wasbased 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 resourcesfor the studied period. This study further annotated thescientific articles according to the modeledtopicsto provide a better searching experiencetoits 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. Keywords: Metadata tagging, DESIDOC Journal of Library and Information Technology(DJLIT), Latent Dirichlet allocation (LDA), Information retrieval, Naive Bayes, Prediction modeling, Support Vector Machine (SVM), Text mining, Topic modeling