| 000 -LEADER |
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| fixed length control field |
03836cam a2200421 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
19539498 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
CITU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230216164125.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr |n||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170306s2017 nju o 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2017010584 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119282099 (pdf) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119282082 (epub) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng. |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.D343 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3/12 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Preferred name for the person |
Kwartler, Ted, |
| Dates associated with a name |
1978- |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Text mining in practice with R / |
| Statement of responsibility, etc |
Ted Kwartler. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, NJ : |
| Name of publisher, distributor, etc |
John Wiley & Sons, |
| Date of publication, distribution, etc |
2017. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (320 pages). |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
computer |
| Media type code |
c |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
online resource |
| Carrier type code |
cr |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 0# - CONTENTS |
|---|
| Formatted contents note |
What is text mining? -- Basics of text mining -- Common text mining visualizations -- Sentiment scoring -- Hidden structures : clustering, string distance, text vectors & topic modeling -- Document classification : finding clickbait from headlines -- Predictive modeling : using text for classifying & predicting outcomes -- The OpenNLP Project -- Text sources. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
A reliable, cost-effective approach to extracting priceless business information from all sources of text<br/><br/>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. <br/><br/>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:<br/><br/>Identify actionable social media posts to improve customer service <br/>Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more <br/>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<br/>Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more<br/>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. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| -- |
600-699 |
| -- |
620 |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on print version record and CIP data provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Text processing (Computer science) |
| 655 ## - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Full text available at Wiley Online Library Click here to view |
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119282105 |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
EBOOK |