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The Resource Practical text analytics : interpreting text and unstructured data for business intelligence, Steven Struhl

Practical text analytics : interpreting text and unstructured data for business intelligence, Steven Struhl

Label
Practical text analytics : interpreting text and unstructured data for business intelligence
Title
Practical text analytics
Title remainder
interpreting text and unstructured data for business intelligence
Statement of responsibility
Steven Struhl
Creator
Contributor
Subject
Genre
Language
eng
Cataloging source
NhCcYBP
Dewey number
658.4/72
Index
no index present
LC call number
HF5415.125
LC item number
.S77 2015
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Marketing science
Label
Practical text analytics : interpreting text and unstructured data for business intelligence, Steven Struhl
Link
http://site.ebrary.com/lib/byuprovo/docDetail.action?docID=11072300
Instantiates
Publication
Note
Description based on print version record
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Machine generated contents note: Preface01 Who should read this book? -- Who should read this book -- Where we find text -- Sense and sensibility in thinking about text -- A few places we will not be going -- Where we will be going from here -- Summary -- References02 Getting ready: capturing, sorting, sifting, stemming and matching -- What we need to do with text -- Ways of corralling words -- Summary -- References03 In pictures: word clouds, wordles and beyond -- Getting words into a picture -- The many types of pictures and their uses -- Clustering words -- Applications, uses and cautions -- Summary -- References04 Putting text together: clustering documents using words -- Where we have been and moving on to documents -- Clustering and classifying documents -- Clustering documents -- Document classification -- Summary -- References05 In the mood for sentiment (and counting) -- Basics of sentiment and counting -- Counting words -- Understanding sentiment -- Summary -- References06 Predictive models 1: having words with regressions -- Understanding predictive models -- Starting from the basics with regression -- Rules of the road for regression -- Divergent roads: regression aims and regression uses -- Practical examples -- Summary -- References07 Predictive models 2: classifications that grow on trees -- Classification trees: understanding an amazing analytical method -- Seeing how trees work, step by step -- CHAID and CART (and CRT, C&RT, QUEST, J48 and others) -- Summary: applications and cautions -- References08 Predictive models 3: all in the family with Bayes Nets -- What are Bayes Nets and how do they compare with other methods? -- Our first example: Bayes Nets linking survey questions and behaviour -- Using a Bayes Net with text -- Bayes Net software: welcome to the thicket -- Summary, conclusions and cautions -- References09 Looking forward and back -- Where we may be going -- What role does text analytics play? -- Summing up: where we have been -- Software and you -- In conclusion -- References Glossary -- Index
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9780749474027
Isbn Type
(electronic bk.)
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic reproduction.
Specific material designation
remote
Label
Practical text analytics : interpreting text and unstructured data for business intelligence, Steven Struhl
Link
http://site.ebrary.com/lib/byuprovo/docDetail.action?docID=11072300
Publication
Note
Description based on print version record
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Machine generated contents note: Preface01 Who should read this book? -- Who should read this book -- Where we find text -- Sense and sensibility in thinking about text -- A few places we will not be going -- Where we will be going from here -- Summary -- References02 Getting ready: capturing, sorting, sifting, stemming and matching -- What we need to do with text -- Ways of corralling words -- Summary -- References03 In pictures: word clouds, wordles and beyond -- Getting words into a picture -- The many types of pictures and their uses -- Clustering words -- Applications, uses and cautions -- Summary -- References04 Putting text together: clustering documents using words -- Where we have been and moving on to documents -- Clustering and classifying documents -- Clustering documents -- Document classification -- Summary -- References05 In the mood for sentiment (and counting) -- Basics of sentiment and counting -- Counting words -- Understanding sentiment -- Summary -- References06 Predictive models 1: having words with regressions -- Understanding predictive models -- Starting from the basics with regression -- Rules of the road for regression -- Divergent roads: regression aims and regression uses -- Practical examples -- Summary -- References07 Predictive models 2: classifications that grow on trees -- Classification trees: understanding an amazing analytical method -- Seeing how trees work, step by step -- CHAID and CART (and CRT, C&RT, QUEST, J48 and others) -- Summary: applications and cautions -- References08 Predictive models 3: all in the family with Bayes Nets -- What are Bayes Nets and how do they compare with other methods? -- Our first example: Bayes Nets linking survey questions and behaviour -- Using a Bayes Net with text -- Bayes Net software: welcome to the thicket -- Summary, conclusions and cautions -- References09 Looking forward and back -- Where we may be going -- What role does text analytics play? -- Summing up: where we have been -- Software and you -- In conclusion -- References Glossary -- Index
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9780749474027
Isbn Type
(electronic bk.)
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic reproduction.
Specific material designation
remote

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