000			02819cam a2200361Ia 4500
003			OCoLC
005			20240809121444.0
008			220427s2022 sz ob 001 0 eng d
020			_a9783030850852
020			_a3030850854
020			_z3030850846
020			_z9783030850845
041			_aEnglish
049			_aMAIN
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223/eng/20220505 _bLAM
100	1		_aLamba, Manika,
100	1		_d1992- _eauthor.
245	1	0	_aText mining for information professionals : _ban uncharted territory / _cManika Lamba, Margam Madhusudhan.
260			_aCham, Switzerland: _bSpringer _c2022.
300			_a1 online resource.
504			_aIncludes bibliographical references and index.
505	0		_a1. The Computational Library -- 2. Text Data and Where to Find Them? -- 3. Text Pre-Processing -- 4. Topic Modeling -- 5. Network Text Analysis -- 6. Burst Detection -- 7. Sentiment Analysis -- 8. Predictive Modeling -- 9. Information Visualization -- 10. Tools and Techniques for Text Mining and Visualization -- 11. Text Data and Mining Ethics.
520			_aThis 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. The book contains 11 chapters with 14 case studies showing 8 different text mining and visualization approaches, and 17 stories. In addition, both a website and a Github account are also maintained for the book. 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. The interactive virtual environment runs case studies based on the R programming language for hands-on practice in the cloud without installing any software. From 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. 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.
650		0	_aText data mining.
650		0	_aInformation retrieval.
700	1		_aMadhusudhan M.
700	1		_q(Margam), _d1970- _eauthor.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-3-030-85085-2
856	4	0	_zConnect to e-book
907			_a.b38541427
942			_2ddc _cBOOKS
999			_c43347 _d43347