He has worked as Deputy Dean Academics and Member of Academic Council at the University of Delhi. Margam Madhusudhan is currently working as a Professor in the Department of Library and Information Science, University of Delhi, India. Her scholarship focuses on the intersections of computational social science, social informatics, information retrieval, services, and management.
She is an active reviewer for more than 17 international journals, including IEEE Access, Scientometrics, Library Hi-Tech, and the Journal of Information Science. She was Editor-at-large for dh+lib (an ACRL Digital Humanities Interest Group project) and was featured in the Information Professionals Share their Top Tips for 2019 blog by the Copyright Clearance Center (CCC). candidate at the Department of Library and Information Science, University of Delhi, India. She is currently serving as the Editor-in-Chief of the International Journal of Library and Information Services (IJLIS), the Elected Standing Committee Member for IFLA Science and Technology Libraries Section, and Newsletter Officer for ASIS&T South Asia Chapter. Additionally, this book will also be helpful to archivists, digital curators, or any other humanities and social science professionals who want to understand the basic theory behind text data, text mining, and various tools and techniques available to solve and visualize their research problems. The interactive virtual environment runs case studies based on the R programming language for hands-on practice in the cloud without installing any software.įrom understanding different types and forms of data to case studies showing the application of each text mining approaches on data retrieved from various resources, this book is a must-read for all library professionals interested in text mining and its application in libraries. They contain the code, data, and notebooks for the case studies a summary of all the stories shared by the librarians/faculty and hyperlinks to open an interactive virtual RStudio/Jupyter Notebook environment. In addition, both a website and a Github account are also maintained for the book. The book contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. This book focuses on a basic theoretical framework dealing with the problems, solutions, and applications of text mining and its various facets in a very practical form of case studies, use cases, and stories.
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