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…mehr
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.
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
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
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