Steven Struhl

Practical Text Analytics (eBook, ePUB)

Interpreting Text and Unstructured Data for Business Intelligence

• Format: ePub

Produktbeschreibung

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. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

Produktdetails

• Verlag: Kogan Page
• Seitenzahl: 272
• Erscheinungstermin: 3. Juli 2015
• Englisch
• ISBN-13: 9780749474027
• Artikelnr.: 43403037

Autorenporträt

Steven Struhl is Principal at Converge Analytic, a marketing and analytics consulting company based in New Jersey. He has experience in consulting and research, specializing in providing effective, practical solutions based on statistical models of decision-making and behavior. His work addresses how buying decisions are made, understanding consumer groups and their motivations, optimizing service delivery and product configurations, and finding the meaningful differences among products and services.

Inhaltsangabe

Preface
01 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
References
02 Getting ready: capturing, sorting, sifting, stemming and matching
What we need to do with text
Ways of corralling words
Summary
References
03 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
References
04 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
References
05 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
References
06 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
References
07 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
08 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
References
09 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