1. Introduction to JMP Text Explorer Platform
Jeff Swartzel and Tracy Desch
With credit to Scott Reese and Jeremy Christman

2. Objectives To introduce Text Exploration via JMP tools
To provide examples of data curation steps
Illustrate ways to quantify text comments
Explore modeling ratings data with quantified text comments

3. Data requirements Stacked data file
One row per comment
Matched ratings
There can be exceptions to this approach.
Make sure that you don’t have duplicate documents in your corpus!

4. Overall process Data preparation aka Curation Analysis Modeling
Tokenizing
Recoding
Phrasing
Stemming
Stopwords
Analysis
Which terms are most common?
What context are terms used?
Which terms appear together? Are there recurring themes?
Modeling
Save Vectors to table
Model building

5. Our Data Source Amazon Products Reviews and Metadata from 1996 to 2014
Found here:
We focused on gourmet food review summaries
“TASTES GOOD AND IS GOOD FOR YOU”
-5 stars
“I wish I could find these in a store instead of online!”
-5 stars
“Not natural/organic at all”
-1 star
“Mixed thoughts”
-3 stars
“bugs all over it”
-2 stars
“Flavorful, great price, and surprisingly not that hot”
-5 stars

6. Key Definitions
Term – smallest piece of text, similar to a word in a sentence
Phrase – collection of terms that occur together
Document – all of the terms in a specific row/column intersection
This is often the panelist’s response to a single question.
Corpus- a collection of documents, a single column
Stemming – the process of removing word endings from words with the same beginning.
“Dogs”, “Doggies”, “Dog” all can be stemmed to “Dog-” so they are counted the same

7. Overall process- bag of words approach
Document term matrix built
A table that is used to COUNT all the terms
Maintained in the background due to size
Very Sparse matrix (not many data points)
Basis for most of the Text Analysis options
The manner in which words are counted can be modified
For example: Yes or No
For example: No occurrence, One occurrence, or Many occurrences
Singular Value Decomposition (will be discussed later…)
A method of dimensionality reduction
Preserves as much information as possible and reduces the number of columns

8. How does it work?
The Document Term Matrix (DTM) is a table of all terms.
Every term is given its own column
Default setting is to create Binary responses for each term:
1 = Yes the term is present in the comment
0 = No the term is NOT present in the comment

9. Counting
There are several ways to ‘count’ the terms. JMP refers to this as “Weighting”
The weighting determines the values that go into the cells of the document term matrix.
Binary:
1 if a term occurs in each document and 0 if not.
Ternary:
2 if a term occurs more than once in each document, 1 if it occurs only once and 0 otherwise.
Frequency:
The count of a term’s occurrence in each document.
Log Freq:
Log10 ( 1 + x ), where x is the count of a term’s occurrence in each document.
TF IDF:
Term Frequency- Inverse Document Frequency. This is the default approach.
A method of counting that accounts for how often a term is used by the total number of uses
TF * log( nDoc / nDocTerm ). The terms in the formula are defined as follows:
TF = frequency of the term in the document
nDoc = number of documents in the corpus
nDocTerm = number of documents that contain the term

10. Why Curation- CURATION
JMP’s bag of words approach uses a frequency count for analysis
Therefore, HOW you count the words matters.
Curation is the process of wrangling the data into a way that is useful for you.
For example, would you count the following as separate terms or the same:
Perfume, Parfume, Pefume, Perfumes, perfumed?
Clean, Kleen, Cleaner, Cleaning lady, Cleaners, cleaned, cleaning?
Perfume, scent, aroma, odor, smell, fragrance?

11. Curation tools CURATION Recode Add phrase Stemming Add stop word
Enables you to change the values for one or more terms. Select the terms in the list before selecting this option.
Always recode before stemming
Add phrase
Adds the selected phrases to the Term List and updates the Term Counts accordingly.
Only added phrases will be included in the analysis and Document Term Matrix
Stemming
Combining words with identical beginnings (stems) by removing the endings that differ.
This results in “jump”, “jumped”, and “jumping” all being treated as the term “jump·”.
Add stop word
Excludes a word that is not providing benefit to the analysis.
For example, if every review contains, “diaper” this does not provide additional benefit to the analysis.
There is a default list of stop words (such as, the, of, or…etc.) that can be modified and saved.
Although stop words are not eligible to be terms, they can be used in phrases.

12. Recode example CURATION Used for the following:
To correct typos or misspellings
Combining synonyms
Grouping terms together based upon category expertise of known themes or topics

13. Common Analysis questions
Which terms are most common?
What context are terms used?
Which terms appear together/ are there recurring themes?
How can I use this in a predictive model?
For modeling:
What are the key drivers?
What are some unique words being said about a product?

14. Topic Analysis, Rotated SVD
“Performs a varimax rotated singular value decomposition of the document term matrix to produce groups of terms called topics.”
In other words…… It takes the Document Term Matrix, which is mostly 0’s, and converts it into a more compact data set where topics are oriented towards a set of words.
Topic analysis is similar to factor analysis.
You need to set the number of vectors, which is how many ‘topics’ you will end up with. Negative values indicate that a term occurs less frequently compared to terms with positive values.

15. Which terms appear together?
ANALYSIS
Rotated SVD
Red hotspot > Topic Analysis, Rotated SVD
Keep all defaults for now
Select OK
In future runs, you could modify the weighting
Let’s start with 20
For real analysis, you would modify this several times to generate a meaningful set

16. A note on Iterations ANALYSIS
Data cleaning will continue after you conduct your SVD.
This most often takes place as: Clean the data, Conduct SVD, Clean the data, Conduct SVD… repeat until your data is most meaningful to you.

17. Which terms appear together?
ANALYSIS
Iterate to find the optimal topics by modifying the following:
Number of topics
Stop words
Consider low frequency words as stop words
Use various approaches on your newly quantified text to improve your understanding of the text:
Partition- shows biggest breaks in the data
Generalized regression- shows model effects
Tabulate then Graphbuilder of topics- shows biggest differences between products

18. How can I use this in a predictive model?
ANALYSIS
How can I use this in a predictive model?
Use SVD to understand themes (like PC or Factor Analysis)
This helps:
Group comments by theme
Discover the common themes much faster
Turn comments into series of continuous factors
Implemented Directly in JMP 14

19. How can I use this in a predictive model?
ANALYSIS
How can I use this in a predictive model?
We will start with the Topic Analysis, Rotated SVD results
Approach:
Curate data to useful topics of interest
Save vectors for each topic to the data table
Use various tools to further analysis and drive understanding of impact

20. Key points to remember:
Text Explorer is intended to combine similar terms, recode terms, and provide understanding on underlying patterns to enable efficient exploration of the comments.
JMP uses a ‘bag of words’ approach- frequency matters, not order.
Iterative steps will take time and effort.
It is still necessary to always explore and read actual verbatims
JMP has “Show Text” tool that can help with this
Category expertise will result in the most robust learnings and insight creation.

21. Overall process (one more time)
Data preparation aka Curation
Tokenizing
Recoding
Phrasing
Stemming
Stopwords
Analysis
Which terms are most common?
What context are terms used?
Which terms appear together? Are there recurring themes?
Modeling
Save Vectors to table
Model building

22. Remember:
JMP Text tools are intended to enable more insight generation by helping to make you more efficient!
Context is critical
Use SHOW TEXT

23. Set Display Options to be default
File > Preferences > Platform > Text Explorer