  The information explosion in the form of ETDs poses the challenge of
management and extraction of appropriate knowledge for decision-making. Thus,
the present study forwards a solution to the above problem by applying topic
mining and prediction modeling tools to 263 ETDs submitted to the PQDT Global
database during 2016-18 in the field of library science. This study was divided
into two phases. The first phase determined the core topics from the ETDs using
Topic-Modeling-Tool (TMT), which was based on latent dirichlet allocation
(LDA), whereas the second phase employed prediction analysis using
RapidMinerplatform to annotate the future research articles on the basis of the
modeled topics. The core topics (tags) for the studied period were found to be
book history, school librarian, public library, communicative ecology, and
informatics followed by text network and trend analysis on the high probability
cooccurred words. Lastly, a prediction model using Support Vector Machine (SVM)
classifier was created in order to accurately predict the placement of future
ETDs going to be submitted to PQDT Global under the five modeled topics (a to
e). The tested dataset against the trained data set for the predictive
performed perfectly.
