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Practical text analytics : interpreting text and unstructured data for business intelligence /

Bibliographic Details
Main Author: Struhl, Steven M.
Corporate Author: Ebook Library
Format: eBook
Language:English
Published: London ; Philadelphia : Kogan Page, 2015.
Series:Marketing science
Subjects:
Marketing > Data processing.
Big data.
Business intelligence.
Marketing research.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of 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 .