Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence

by Steven Struhl

Overview

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.

Product Details

ISBN-13:	9780749474027
Publisher:	Kogan Page, Ltd.
Publication date:	07/03/2015
Series:	Marketing Science
Sold by:	Barnes & Noble
Format:	eBook
Pages:	272
File size:	10 MB

About the Author

Steven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.

Dr. Steven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specialising in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimising service delivery and product configurations and finding the meaningful differences among products and services.

Steven Struhl also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.

Table of Contents

• • Chapter - 01: Who should read this book? And what do you want to do today?;
  • Chapter - 02: Getting ready: capturing, sorting, sifting, stemming and matching;
  • Chapter - 03: In pictures: word clouds, wordles and beyond;
  • Chapter - 04: Putting text together: clustering documents using words;
  • Chapter - 05: In the mood for sentiment (and counting) ;
  • Chapter - 06: Predictive models 1: having words with regressions;
  • Chapter - 07: Predictive models 2: classifications that grow on trees;
  • Chapter - 08: Predictive models 3: all in the family with Bayes Nets;
  • Chapter - 09: Looking forward and back

Editorial Reviews

"A fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk."

Darren Ingram Media Darren Ingram

"Steven Struhl has an incredible knack for demystifying complex analyses and analytic software, and making it accessible to those who are interested in what it does without delving too deeply into the incomprehensible elements of how it works. In his new book, Dr. Struhl takes on text analytics. I found the chapter on Bayes Nets particularly useful. In it he shows quite convincingly that, in some cases, they do a much better job with text than other predictive methods. He provides a story through crystal-clear examples that are immediately interesting and easy to follow."

Larry Durkin

"As I've been evaluating text analytics materials lately for my data science education engagements, much of what I've found published on this subject is written from a very academic and technical perspective that is not very approachable for someone that doesn't have a fairly deep expertise in statistics, math and programming. This book solves that disconnect. A welcome addition to any data scientist's library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended."

Inside Big Data Daniel D. Gutierrez

"Steven provides a broad and fair context in which to understand textual analysis in a very readable and informative way. I'm confident this would provide great value to anyone with an interest in the Internet and textual analysis, researcher and non-researcher alike."

Darrin Helsel

"Textual analysis has recently become a useful research methodology, of great interest to both academics and practitioners. Dr. Steven Struhl provides relevant and lucid discussion of the topic, highlighting the fundamental issues involved in preparing, analyzing, and presenting textual data for meaningful interpretations. A very interesting and timely contribution that should be of interest to a wide range of audiences."

Dr. Jehoshua Eliashberg

"[A] fascinating, if not rather specialist book, which aims to be an accessible guide to the world of text analytics and data analysis for marketing folk."

Darren Ingram Media - Darren Ingram

"As I’ve been evaluating text analytics materials lately for my data science education engagements, much of what I’ve found published on this subject is written from a very academic and technical perspective that  is not very approachable for someone that doesn’t have a fairly deep expertise in statistics, math and programming. This book solves that disconnect...A welcome addition to any data scientist’s library. In addition, the timely nature of the subject should provide much food-for-thought as the rise in interest in unstructured data processing techniques continues to be of interest. Highly recommended."

Inside Big Data - Daniel D. Gutierrez