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TitlePractical text mining and statistical analysis for non-structured text data applications
Author Gary Miner ... [et al.]
Imprint Waltham, MA : Academic Press, c2012
Descript xl, 1053 p. : ill. ; 25 cm. + 1 DVD-ROM (4 3/4 in.)

SUMMARY

"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. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications 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"-- Provided by publisher


CONTENT

Basic text mining principles : The history of text mining ; The seven practice areas of text analytics ; Conceptual foundations of text mining and preprocessing steps ; Applications and use cases for text mining ; Text mining methodology ; Three common text mining software tools -- Introduction to the tutorial and case study section of this book -- Advanced topics : Text classification and categorization ; Prediction in text mining: the data mining algorithms of predictive analytics ; Entity extraction ; Feature selection and dimensionality reduction ; Singular value decomposition in text mining ; Web analytics and web mining ; Clustering words and documents ; Leveraging text mining in property and casualty insurace ; Focused web crawling ; The future of text and web analytics


Data mining

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