Description
Book SynopsisSteven 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.
Trade Review"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, Sebastian S. Kresge Professor of Marketing, Professor of Operations and Information Management, Wharton University *
"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, Co-Founder and Principal of Distill Research LLC, and Research Chair, American Marketing Association, Portland Chapter *
"
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, Principal, MSP Analytics *
"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." * Daniel D. Gutierrez, Inside Big Data *
"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, Darren Ingram Media *
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
