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.

Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity

Abstract With the advent of social media, people have found new ways through which they can express their views, opinions, and beliefs . This study presents an interdisciplinary nature of research where sentiment analysis is applied to the economics discipline of productivity as an experimental study to introduce new service for libraries’ users. Firstly, data were retrieved from Twitter on 20 different queries related to productivity using RapidMiner platform and then sentiment analysis was performed employing AYLIEN Text Analysis Software.

Application of Topic Mining and Prediction Modeling Tools for Library and Information Science Journals

Abstract This chapter presents a method for analyzing text data called topic modeling and applying it to the field of Library and Information Science. It describes the importance and usage of topic mining for researchers and librarians. An experiment study is also covered which applies topic modeling in a real scenario, where five model topics for the articles published in DESIDOC Journal of Library and Information Technology for the year 2017 using Topic-Modeling-Toolkit and prediction modeling is constructed using RapidMiner toolbox.