1. Text Mining with JMP Pro 13: A Case Study
Mia Stephens –
Who is familiar with JMP?
JMP 12 release
Who am I
Copyright © 2010, SAS Institute Inc. All rights reserved.

2. What is JMP (and JMP Pro)?
Statistical Discovery Software from SAS
Developed in 1989
For teaching and doing
Comprehensive
Basic
Advanced
Extendible
powerful scripting language
application and add-in builders
Excel, R, MATLAB, and SAS
Visual, dynamic and interactive
JMP Pro – Advanced tools for analytics and modeling
Runs natively
By its very nature, JMP is visual, interactive and dynamic, which makes it an ideal tool for teaching and learning statistical concepts.
JMP has a programming language, which is not required to use JMP, but allows users to expand applications of JMP
Copyright © 2010, SAS Institute Inc. All rights reserved.

3. Agenda Introduction to Text Mining General Workflow
Simple Example – Pet Survey
Case Study: Toronto Casino Survey
Reference: A practical guide to text mining with topic extraction, Karl, Wisnowski, and Rushing, 2015
Citation: WIREs Comput Stat 2015, 7:326–340. doi: /wics.1361

4. Text Mining: General Workflow
Define the problem
Collect/compile the data (unstructured text, and other relevant information)
Process/prepare the text
Correct spelling/capitalization (Recode)
Combine words with same root (Stemming)
Remove common words, symbols, numbers,… (Stopwords)
Transform text (Document Term Matrix)
Explore clusters and topics to identify themes
Group similar documents and words
*Create new variables for predictive modeling

5. What is a Document Term Matrix?
Take the following “documents”
Find the number of unique terms
Create indicator variables for each term
The prices at Lowes are amazing
Finding help in Lowes is a problem

6. Simple Example: Pet Survey

7. Case Study: Toronto Casino Survey

8. Case Study: Toronto Casino Survey
Data exploration
Are respondents generally in favor of or opposed to the casino?
Does the response depend on gender or age?
Text Explorer
Term and Phrase Lists: What are the most frequently used terms and phrases? In what context are the terms and phrases used?
Word Cloud: Do those in favor use different words/phrases than those opposed?
Clustering: Which terms tend to appear together? Can similar documents be grouped together?
Topic Analysis: What are the recurring themes?
Predictive Modeling
Can the topics or terms be used to predict whether a respondent was in favor of or opposed to the casino?

9. JMP Text Explorer Resources
Info Kit
Includes a webinar and book chapter on text mining
Short guides (in the JMP Learning Library)
jmp.com/learn
Videos: 
General overview discussion of text explorer (Heath Rushing) with a simple example: 

10. Mia.stephens@jmp.com jmp.com/academic
Discussion and Q&A
jmp.com/academic