Text Mining Algorithms And Applications
Download Text Mining Algorithms And Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Text Mining Algorithms And Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Text Mining: Algorithms and Applications
Author: Kenneth Cosh
language: en
Publisher: Chiang Mai University
Release Date: 2020-02-01
We are living in an era where a vast amount of information is created every second of every day. Meaningful information is the most valuable asset a person, an organization or a country can have. Relevant knowledge can be used to increase revenues, negotiate a better deal, or even win an election. But there are always questions of “how” and “where” to harvest useful knowledge from extensive information. This book offers its readers a way to use machine learning and data mining techniques to acquire worthwhile knowledge from a colossal amount of information. There are many examples that make this book easy to read and follow. Since, the author is an active researcher in the field, readers of this book will receive current techniques in the field of data mining. This is a book for a person who wants to learn how to extract fruitful knowledge from social media and the Internet.
Mining Text Data
Author: Charu C. Aggarwal
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-02-03
Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. - Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible - Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com - Glossary of text mining terms provided in the appendix