Metadata Tagging of Library and Information Science Theses Shodhganga (2013-2017)

Abstract

Electronic Theses and Dissertations (ETDs) poses the challenge of managing and extraction of appropriate knowledge for decision making. To tackle the same, topic modeling was first applied to Library and Information Science (LIS) theses submitted to Shodhganga (an Indian ETDs digital repository) to determine the five core topics/tags and then the performance of the built model based on those topics/tags were analyzed. Using a Latent Dirichlet Allocation based Topic-Modeling-Toolkit, the five core topics were found to be information literacy, user studies, scientometrics, library resources and library services for the epoch 2013-2017 and consequently all the theses were summarized with the presence of their respective topic proportion for the tags/topics. A Support Vector Machine (Linear) prediction model using RapidMiner toolbox was created and showed 88.78% accuracy with 0.85 kappa value.