Presentation at the 11th Annual Text and Social Analytics Summit - Cambridge, MA. Integrate unstructured data within a relational database: Learn the feasibility, prototyping, value added, and the goals of Text Analytics. Understand how much data you have and the architecture necessary to leverage existing technology that goes along with your existing relational structure (Oracle, SAS, SQL Server, DB/2, Postgre, MySQL and others). Learn how to utilize sentiment analysis to determine propensity to churn. A best in practice discussion of statistic techniques, clustering, and association.

1. PRACTICAL TEXT
MINING WITH SQL
USING RELATIONAL
DATABASES
Ralph Winters
Data Architect,
Actuarial Business Intelligence
EmblemHealth
June 5th, 2013
11th Annual Text and Social Analytics Summit
Cambridge, MA

2. RDMS TODAY
Gartner - clients tell us that
combining scored, processed
‘outside data’ with data inside our
relational databases is where all
the added value is.
IDC -RDMD database
management systems are
expected to nearly double in
market growth by 2016 driven by
intelligence demands and
expabusiness nded adoption to
tackle big data and unstructured
information streams
The relational database
management systems (RDBMS)
market continues to confound the
skeptics by maintaining strong
growth characteristics despite the
belief by some that the market has
become 'saturated‘ or that it will
be weakened by newer Big Data
technologies
Inmon:
listen carefully to the “big data”
vendors and this is what you hear:
“Let’s get rid of relational.” It is like
courtiers in the castle whispering,
“The king must die.” What’s going
on here?.

3. Why a relational DB?
Why a
relational
Database?
Marry
Structured +
Unstructured
Data
More suitable
for statistical
analytics
(matrices)
Leverage
existing
familiar
widespread
technology
Improving of
predictive
Models
Referential
Integrity
Integrated
Text/Data
Mining

4. Feasibility
What do I need
to know?
Costs
Benefits/Risks Industries
Adding
Value?

5. RDMS
File Interfaces
(XML,CSV)
ODBC/JDBC/DBI
Text Vendor
supplied Connector
Hadoop
Connectors
(SAS, Oracle)
Open Source Text
Mining Tools
(R, Java, Perl,
LingPipe)
In-Database Text
Mining Algorithms
(Oracle*Text,SAS
Text Miner,SQL
Server Text
Miniing)
RDMS
Internal/External
Connections

6. ANGRY Customer Comments
Short Tailed
Sampling
Not for Long
Tailed Data
Comment
- KardCo Premier Credit Card Promo Scam . I recently received
an KardCo promo promising 25,000 bonus points if you sign up
for the KardCo Premier Card and spend $2000 in the 1st three
months. and so i call in and apply ...got APPROVED...two
weeks later ..
Posting on your site DEFINITELY HELPED (it was pointed out by
retailer), and sped up response after 6 weeks of mulling
around BEFORE we posted our complaint.
$100 restaurant certificates
15 days ago I opened a cc w/ KardCo. I thought I did my
research on which company is the best, boy was I wrong. I go
to use my card for the 1st time lastnight & its declined. Ok.... I
call KardCo from the store and I'm placed on hold for 20 mins.
Finally I speak to an awful women who tells me my debt to
income ratio is too high and I have too many inquires. I pull my
credit report once I get home I pull the one from when I
opened the card and the most recent one. My revolving debt
$100, my credit score increased from 738 to 740 and 96% of
my credit is currently available....
1-800 Customer Service NOT LOCATED IN US!
2 years in a row they don't send me my rewards check

7. Full Text Search
Built in to many RDMS
Needs Indexing
Can be Slow
Necessary in some Applications
Complements Categorization
Oracle:
SELECT SCORE(1), comment,
issue_date from custdb
WHERE CONTAINS(text, 'APR', 1) > 0
AND issue_date >= ('01-OCT-97')
ORDER BY SCORE(1) DESC;
Operators: Like, Contains, Regex,
Sounds Like, Distance Measures

8. Term Doc
Best 1
Customer 1
Service 1
Highly 2
Recommended 2
Parse Terms from Each
Row
Remove StopWords
Cross Reference
Document ID & Term
Numbers
Output New
“Structured”
Table
Map Unstructured-to-Structured
Doc Term1 Term2 Term3 Term4
1 The Best Customer Service
2 Is Highly Recommended
“Wasted
Space”

9. Extended
SQL
User Defined
Functions
Stored
Process
Many Methods to Pivot Data
select
regexp_split_to_table(lower(line), 's+')
as word
from
customer_comments

10. “Words” Table
One Row for each
term in Doc.
Term Index
Number
“Document
ID”
Verbatim
Term Index
+1
Term Index -
1
Must handle
Negation!

11. Term document matrix
Harder to do
analysis in SQL
Wasted
Space
Weight Terms Discard Terms

12. Term Weighting in SQL
• Log(Number of
Docs / Number of
Docs which
contain term)
Calculate
IDF
• Number of times
Term occurs in
document
Calculate
Term Freq
• Mulitply IDF *TF
• Sort by High
values
• Select Top N
features
Calculate
tfidf
create table idf as select
word,num_docs.value as
numdocs,doc_freq.value as
docfreq,
log10(num_docs.value/doc_freq.
value) as idf
from doc_freq,WORK.num_docs
order by idf;
create table doc_freq as
select word,count(distinct
id) as value
from WORDS
group by word
order by value;
create table num_docs as
select count(distinct id)
as value
from WORDS;
Words
Table
Top N
Words
Pivot
on
Rows

13. Top N Weighted Words Matrix –
Ranked by Highest TD/IDF

14. select a.ID,
(compress(a.word) || ' ' ||
compress(b.word)) as pair,
from words a , words b
where a.ID=b.ID and (a.no=b.no_prev)
order by pair;
Generating Bigrams

15. Bigrams Output
 Run Frequencies on Terms
 Gift Card occurs more
frequently than expected
 Consider incorporating into
Taxonomy
SAMPLE BIGRAM COUNT
EXPECTED
COUNT
Have Been 2326
Gift Card 2910
Called Kardo 2119
Kardco Card 3125
Customer Service 3630
Credit Card 2429
Member Since 1013
Credit Limit 1310
Starlight Card 115
Kardco Customer 86
Big Ram

16. Do repeat callers signal Churn?
 ..
Research shows
improved predictive
Models
performance
Correlate with
Satisfaction Scores
Relevant Keywords
First Call Responders
pair Status Count satisfaction
CUSTOMER SERVICE A 27 8.47
GIFT CARD A 25 8.34
KARDCO CARD A 24 8.79
CREDIT CARD A 15 8.62
WITH KARDCO A 13 8.28
TRANSFERRED AGAIN I 12 8.30
CREDIT LIMIT A 11 8.35
FROM KARDCO A 10 8.50
PREMIER CARD A 9 8.42
WITH KARDCO I 9 8.48
THREE MONTHS A 9 8.37
CUSTOMER SERVICE I 9 8.36

17. select distinct comm1 from Customer Comments
Where prxmatch("m/2nd|3rd|again|resolve/oi",comm1) >0
Customer comment Sat
Hotel cant resolve my dispute. I'm going to cancel 4
Never resolved. Still waiting for a call back 3
So Completely Unhappy with KardCo. It took 3 calls to the service center to finally resolve my billing
problem
5
They gave me a 2nd chance to pay my bill 9
This complaint was never resolved to begin with 5
This is the 2nd year in a row that KardCo said they mailed my rewards refund that I have yet to
recieve. Same Pattern every year, I stop getting paper statements in December even though I am
signed up for them and I never get my Check. Then I mysteriously start getting paper statements
again after the period they say they will cut the checks and tell me i am no longer eligable.
6
This is the 3rd time I have complained about this and I may have to take my business elsewhere! 4
Transferred again for the 2nd time. I can't believe it. What happened to Cindy? 1
When ever I compare customer service between companies KardCo is the PREMIER standard. They
are on call 24 hours a day. Their operators are friendly and easy to speak with. They are always on the
customers side and they always work at a situation until they resolve the issue.
10
Looking for the Repeat Callers
Some False positive
Terms “resolve” and “2nd” can be positive

18. Satisfaction
Score
Outstanding
Balance
Predict Churn
Churn
Improves
Implement
New Scripts for
call center
Number of
Times Called
Select all comments
with “Gift Card”
Insert Keys into
Model Table
Join new Model with
existing model tables
How Text Analytics can improve Predictive
Model

19. STANDARD CLASSIFICATIONS
Advertising and
marketing Credit determination
Application
processing delay
Credit line
Increase/decrease
APR or interest
rate Credit reporting
Arbitration
Customer service /
Customer relations
Balance transfer Delinquent account
Balance transfer
fee
Forbearance / Workout
plans
Bankruptcy
Identity theft / Fraud /
Embezzlement
Billing disputes Late fee
Billing statement Other
Cash advance Other fee
Cash advance
fee Overlimit fee
Closing/Cancellin
g account Payoff process
Collection debt
dispute Privacy
Collection
practices Rewards
Convenience
checks Sale of account
Credit card
protection / Debt
protection Transaction issue
Unsolicited issuance of
credit card
Add “Gift Card”
as a
Classification

20. “Tweak”
Taxonomy
Apply Auto
Classification
Evaluate
according to
GOLD
Standard
Apply CRISP
or SEMMA
Methodology
and Repeat
Validation
CAT Count Customer
Service
Baseline
Average
Spend
ADV 15 15 15,483
APR 12 12 13,308
BANKRUPT 1 1 13,108
BILLDISP 6 6 12,682
BILLSTAT 6 6 10,617
COLL 1 1 17,720
CUSTSERV 25 25 14,725
DELAY 1 1 13,334
FRAUD 13 13 15,162
GIFTCARD 18 18 16,107
LATEFEE 3 3 18,989
LINEADJ 4 4 13,762
OTHER 125 125 18,482
OTHERFEE 15 15 10,153
PROT 1 1 17,808
REFUND 2 2 16,473
REWARDS 10 10 10,918
TRANS 1 1 14,224
TRAVEL 8 8 10,355
“There is no globally best method for
(automated) text analysis”

21. Other Types of Classification
Select id,comm
Case
When compged(‘High Interest Rate APR’,comm1 < 300 then ‘APR’
When compged(‘Best Customer Service’,comm1 < 300 then
‘DELIGHT’
Else ‘OTHER’ end as CAT from CUSTOMER_COMMENTS
Classify by
Keyword
Pairs
Regular Expressions
Boolean
Distance
Functions
Fuzzy
Matching
Regex
Bayesian
algorithms

22. Sentiment – Can be easy, can be hard!
Words
Table
Join to
Polarity
Dictionary
Assign +1 to
Positive /-1
to Negative
Sentiment
Score
Use Top N
Weighted
Terms
Use First and
Last Sentences
Vector Size CPU? Complexity Normalized
Use In-Memory
Lookups
Customized
Dictionary
Bayesian
Classifier in SQL

23. CAT Count
Average Satisfaction Neg
Pct
Not
Neg
PctSpend
ADV 15 15,483 7.5 49 51
APR 12 13,308 7.2 72 28
FRAUD 13 15,162 5.2 61 39
GIFTCARD 18 16,107 8.9 24 76
LATEFEE 3 18,989 7.0 12 88
Sentiment – Correlation
Correlating Sentiment Scores with other database metrics can support
hypothesis

24. THANK YOU!
Contact:
R_winters@emblemhealth.com
www.linkedin.com/in/ralphwinters