國立虎尾科技大學 |

Text mining in practice with R

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text mining in practice with R/ Ted Kwartler.
Author:	Kwartler, Ted,
Published:	Chichester, West Sussex, UK ;John Wiley & Sons, : 2017.,
Description:	1 online resource.
Subject:	Data mining. -
Online resource:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119282105
ISBN:	9781119282105

Text mining in practice with R
Kwartler, Ted,1978-

Text mining in practice with R[electronic resource] /Ted Kwartler. - 1st ed. - Chichester, West Sussex, UK ;John Wiley & Sons,2017. - 1 online resource.

Includes bibliographical references and index.

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 tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You'll learn how to: -Identify actionable social media posts to improve customer service -Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more -Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files -Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies' data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents-until now.

ISBN: 9781119282105

LCCN: 2017010584Subjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.3/12

Text mining in practice with R
LDR:03299cam a2200325 a 4500 001 908076
003 OCoLC
005 20180330091527.7
006 m o d
007 cr |||||||||||
008 190510s2017 enk ob 001 0 eng
010 $a 2017010584
020 $a 9781119282105 $q (electronic bk.)
020 $a 1119282101 $q (electronic bk.)
020 $a 9781119282099 $q (pdf)
020 $a 1119282098 $q (pdf)
020 $a 9781119282082 $q (epub)
020 $a 111928208X $q (epub)
020 $z 9781119282013 $q (cloth)
035 $a 974796237
040 $a DLC $b eng $c DLC $d OCLCF $d DG1 $d YDX $d N $d EBLCP $d IDEBK $d TEFOD $d OTZ $d MERER $d OCLCO $d OCLCQ $d UPM $d RECBK $d COO $d DEBSZ $d OCLCQ $d UAB $d K6U $d CASUM $d OCLCO $d LIV $d D6H
050 0 0 $a QA76.9.D343
082 0 0 $a 006.3/12 $2 23
100 1 $a Kwartler, Ted, $d 1978- $3 1177846
245 1 0 $a Text mining in practice with R $h [electronic resource] / $c Ted Kwartler.
250 $a 1st ed.
260 $a Chichester, West Sussex, UK ; $a Hoboken, NJ : $b John Wiley & Sons, $c 2017.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
520 $a 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 tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You'll learn how to: -Identify actionable social media posts to improve customer service -Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more -Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files -Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies' data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents-until now.
588 $a Description based on print version record.
650 0 $a Data mining. $3 528622
650 0 $a Text processing (Computer science) $3 528615
650 1 2 $a Data Mining. $3 572983
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119282105