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Practical Text Mining and Statistical Analysis for Non structured Text Data Applications

Practical Text Mining and Statistical Analysis for Non structured Text Data Applications
  • Author : Gary Miner
  • Publsiher : Academic Press
  • Release : 25 February 2021
  • ISBN : 012386979X
  • Pages : 1053 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKPractical Text Mining and Statistical Analysis for Non structured Text Data Applications

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. -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


Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
  • Author : Gary Miner
  • Publisher : Academic Press
  • Release : 25 February 2021
GET THIS BOOKPractical Text Mining and Statistical Analysis for Non-structured Text Data Applications

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


Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
  • Author : Gary Miner,John Elder IV,Andrew Fast,Thomas Hill,Robert Nisbet,Dursun Delen
  • Publisher : Academic Press
  • Release : 25 January 2012
GET THIS BOOKPractical 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


Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

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


Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
  • Author : Robert Nisbet,Gary Miner,Ken Yale
  • Publisher : Elsevier
  • Release : 09 November 2017
GET THIS BOOKHandbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and


Text Mining and Analysis

Text Mining and Analysis
  • Author : Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla
  • Publisher : SAS Institute
  • Release : 22 November 2014
GET THIS BOOKText Mining and Analysis

Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This


Practical Text Analytics

Practical Text Analytics
  • Author : Steven Struhl
  • Publisher : Kogan Page Publishers
  • Release : 03 July 2015
GET THIS BOOKPractical Text Analytics

In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals


Text Mining in Practice with R

Text Mining in Practice with R
  • Author : Ted Kwartler
  • Publisher : John Wiley & Sons
  • Release : 12 May 2017
GET THIS BOOKText Mining in Practice with R

A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the


The Text Mining Handbook

The Text Mining Handbook
  • Author : Ronen Feldman,James Sanger
  • Publisher : Cambridge University Press
  • Release : 25 February 2021
GET THIS BOOKThe Text Mining Handbook

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook


Programming Collective Intelligence

Programming Collective Intelligence
  • Author : Toby Segaran
  • Publisher : "O'Reilly Media, Inc."
  • Release : 16 August 2007
GET THIS BOOKProgramming Collective Intelligence

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes


Text Mining with R

Text Mining with R
  • Author : Julia Silge,David Robinson
  • Publisher : "O'Reilly Media, Inc."
  • Release : 12 June 2017
GET THIS BOOKText Mining with R

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating


Practical Predictive Analytics and Decisioning Systems for Medicine

Practical Predictive Analytics and Decisioning Systems for Medicine
  • Author : Linda Miner,Pat Bolding,Joseph Hilbe,Mitchell Goldstein,Thomas Hill,Robert Nisbet,Nephi Walton,Gary Miner
  • Publisher : Academic Press
  • Release : 27 September 2014
GET THIS BOOKPractical Predictive Analytics and Decisioning Systems for Medicine

With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety,


Ensemble Methods in Data Mining

Ensemble Methods in Data Mining
  • Author : Giovanni Seni,John Elder
  • Publisher : Morgan & Claypool Publishers
  • Release : 07 July 2010
GET THIS BOOKEnsemble Methods in Data Mining

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees


Practical Text Analytics

Practical Text Analytics
  • Author : Murugan Anandarajan,Chelsey Hill,Thomas Nolan
  • Publisher : Springer
  • Release : 19 October 2018
GET THIS BOOKPractical Text Analytics

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a


Text Data Management and Analysis

Text Data Management and Analysis
  • Author : ChengXiang Zhai,Sean Massung
  • Publisher : Morgan & Claypool
  • Release : 30 June 2016
GET THIS BOOKText Data Management and Analysis

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied


Data Mining and Statistical Analysis Using SQL

Data Mining and Statistical Analysis Using SQL
  • Author : John Lovett,Robert P. Trueblood
  • Publisher : Apress
  • Release : 01 January 2008
GET THIS BOOKData Mining and Statistical Analysis Using SQL

This book is not just another theoretical text on statistics or data mining. Instead, it's designed for database administrators who want to buttress their understanding of statistics to support data mining and customer relationship management analytics and who want to use Structured Query Language (SQL). Each chapter is independent and self-contained with examples tailored to business applications. Each analysis technique is expressed in a mathematical format that lends itself to coding either as a database query or as a Visual