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Practical Text Mining with SQL using Relational Databases

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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.

Published in: Technology

Practical Text Mining with SQL using Relational Databases

  1. 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. 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. 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. 4. Feasibility What do I need to know? Costs Benefits/Risks Industries Adding Value?
  5. 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. 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. 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. 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. 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. 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. 11. Term document matrix Harder to do analysis in SQL Wasted Space Weight Terms Discard Terms
  12. 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. 13. Top N Weighted Words Matrix – Ranked by Highest TD/IDF
  14. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 24. THANK YOU! Contact: R_winters@emblemhealth.com www.linkedin.com/in/ralphwinters

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