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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. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">01 </span><span xmlns="" xmlns:epub="" class="BOLD">Who should read this book? And what do you want to do today?</span>
    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
  2. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">02 </span><span xmlns="" xmlns:epub="" class="BOLD">Getting ready: capturing, sorting, sifting, stemming and matching</span>
    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
  3. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">03 </span><span xmlns="" xmlns:epub="" class="BOLD">In pictures: word clouds, wordles and beyond</span>
    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
  4. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">04 </span><span xmlns="" xmlns:epub="" class="BOLD">Putting text together: clustering documents using words</span>
    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
  5. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">05 </span><span xmlns="" xmlns:epub="" class="BOLD">In the mood for sentiment (and counting)</span>
    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
  6. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">06 </span><span xmlns="" xmlns:epub="" class="BOLD">Predictive models 1: having words with regressions</span>
    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
  7. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">07 </span><span xmlns="" xmlns:epub="" class="BOLD">Predictive models 2: classifications that grow on trees</span>
    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
  8. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">08 </span><span xmlns="" xmlns:epub="" class="BOLD">Predictive models 3: all in the family with Bayes Nets</span>
    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
  9. <span xmlns="" xmlns:epub="" class="TOCCHAP-NUM">09 </span><span xmlns="" xmlns:epub="" class="BOLD">Looking forward and back</span>
    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