Text Analysis of ETDs in ProQuest Dissertations and Theses (PQDT) Global (2016-2018)

TL;DR

This study applied topic modeling and prediction techniques to analyze and forecast research topics in ETDs from PQDT Global (2016-2018), revealing core themes and enabling future research predictions.

Contribution

It introduces a combined approach using LDA-based topic modeling and SVM prediction to analyze and forecast ETD research topics in library science.

Findings

Identified core topics like book history and informatics.

Developed a prediction model with high accuracy.

Demonstrated effective topic extraction and future research prediction.

Abstract

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.