Practical Text Analytics - Steven Struhl
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Présentation Practical Text Analytics de Steven Struhl Format Broché
- Livre Économie
Résumé :
In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence.
By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation.
Biographie: Sommaire: 08 Predictive models 3: all in the family with Bayes Nets 09 Looking forward and back Glossary
Preface01 Who should read this book? And what do you want to do today?
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
Missing the simple frame with social media
How do I do sentiment analysis?
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
Optimal recoding
CHAID and CART (and CRT, C&RT, QUEST, J48 and others)
Summary: applications and cautions
References
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
References
Where we may be going
What role does text analytics play?
Summing up: where we have been
Software and you
In conclusion
References
Index
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