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Practical Text Analytics

Book Description

Practical Text Analytics explains approaches to text analytics in a way that is grounded in business reality so marketers can easily apply tools and techniques to add value to their companies.

Table of Contents

  1. Cover
  2. Title Page
  3. Dedication
  4. Contents
  5. Preface
  6. 01 Who should read this book? And what do you want to do today?
    1. Who should read this book
    2. Where we find text
    3. Sense and sensibility in thinking about text
    4. A few places we will not be going
    5. Where we will be going from here
    6. Summary
    7. References
  7. 02 Getting ready: capturing, sorting, sifting, stemming and matching
    1. What we need to do with text
    2. Ways of corralling words
    3. Factor analysis: introducing our first analytical method
    4. Summary
    5. References
  8. 03 In pictures: word clouds, wordles and beyond
    1. Getting words into a picture
    2. The many types of pictures and their uses
    3. Clustering words
    4. Applications, uses and cautions
    5. Summary
    6. References
  9. 04 Putting text together: clustering documents using words
    1. Where we have been and moving on to documents
    2. Clustering and classifying documents
    3. Clustering documents
    4. Document classification
    5. Summary
    6. References
  10. 05 In the mood for sentiment (and counting)
    1. Basics of sentiment and counting
    2. Counting words
    3. Understanding sentiment
    4. Missing the sample frame with social media
    5. How do I do sentiment analysis?
    6. Summary
    7. References
  11. 06 Predictive models 1: having words with regressions
    1. Understanding predictive models
    2. Starting from the basics with regression
    3. Rules of the road for regression
    4. Divergent roads: regression aims and regression uses
    5. Practical examples
    6. Summary
    7. References
  12. 07 Predictive models 2: classifications that grow on trees
    1. Classification trees: understanding an amazing analytical method
    2. Seeing how trees work, step by step
    3. Optimal recoding
    4. CHAID and CART (and CRT, C&RT, QUEST, J48 and others)
    5. Summary: applications and cautions
    6. References
  13. 08 Predictive models 3: all in the family with Bayes Nets
    1. What are Bayes Nets and how do they compare with other methods?
    2. Our first example: Bayes Nets linking survey questions and behaviour
    3. Using a Bayes Net with text
    4. Bayes Net software: welcome to the thicket
    5. Summary, conclusions and cautions
    6. References
  14. 09 Looking forward and back
    1. Where we may be going
    2. What role does text analytics play?
    3. Summing up: where we have been
    4. Software and you
    5. In conclusion
    6. References
  15. Glossary
  16. Index
  17. Copyright