Taming Text: An Introduction to Text Mining CAS 2006 Ratemaking Seminar

1. Taming Text: An Introduction to Text MiningCAS 2006 Ratemaking Seminar Prepared by Louise Francis Francis Analytics and Actuarial Data Mining, Inc. April 1, 2006 Louise_francis@msn.com www.data-mines.com

2. Objectives • Present a new data mining technology • Show how the technology uses a combination of • String processing functions • Common multivariate procedures available in statistical most statistical software • Present a simple example of text mining • Discuss practical issues for implementing the methods

3. Actuarial Rocket Science • Sophisticated predictive modeling methods are gaining acceptance for pricing, fraud detection and other applications • The methods are typically applied to large, complex databases • One of the newest of these is text mining

4. Supervised learning Most common situation A dependent variable Frequency Loss ratio Fraud/no fraud Some methods Regression CART Some neural networks Unsupervised learning No dependent variable Group like records together A group of claims with similar characteristics might be more likely to be fraudulent Ex: Territory assignment, Text Mining Some methods Association rules K-means clustering Kohonen neural networks Major Kinds of Modeling

5. Text Mining: Uses Growing in Many Areas ECHELON Program

6. Lots of Information, but no Data

7. Example: Claim Description Field

8. Objective • Create a new variable from free form text • Use words in injury description to create an injury code • New injury code can be used in a predictive model or in other analysis

9. A Two - Step Process • Use string manipulation functions to parse the text • Search for blanks, commas, periods and other word separators • Use the separators to extract words • Eliminate stopwords • Use multivariate techniques to cluster like terms together into the same injury code • Cluster analysis • Factor and Principal Components analysis

10. Parsing a Claim Description Field With Microsoft Excel String Functions

11. Extraction Creates Binary Indicator Variables

12. Eliminate Stopwords • Common words with no meaningful content

13. Stemming: Identify Synonyms and Words with Common Stem

14. Dimension Reduction

15. The Two Major Categories of Dimension Reduction • Variable reduction • Factor Analysis • Principal Components Analysis • Record reduction • Clustering • Other methods tend to be developments on these

16. Correlated Dimensions

17. Clustering • Common Method: k-means and hierarchical clustering • No dependent variable – records are grouped into classes with similar values on the variable • Start with a measure of similarity or dissimilarity • Maximize dissimilarity between members of different clusters

18. Dissimilarity (Distance) Measure – Continuous Variables • Euclidian Distance • Manhattan Distance

19. Binary Variables

20. Binary Variables • Sample Matching • Rogers and Tanimoto

21. K-Means Clustering • Determine ahead of time how many clusters or groups you want • Use dissimilarity measure to assign all records to one of the clusters

22. Hierarchical Clustering • A stepwise procedure • At beginning, each records is its own cluster • Combine the most similar records into a single cluster • Repeat process until there is only one cluster with every record in it

23. Hierarchical Clustering Example

24. How Many Clusters? • Use statistics on strength of relationship to variables of interest

25. A Statistical Test for Number of Clusters • Swartz Bayesian Information Criterion

26. Final Cluster Selection

27. Use New Injury Code in a Logistic Regression to Predict Serious Claims

28. Software for Text Mining-Commercial Software • Most major software companies, as well as some specialists sell text mining software • These products tend to be for large complicated applications, such as classifying academic papers • They also tend to be expensive • One inexpensive product reviewed by American Statistician had disappointing performance

29. Software for Text Mining – Free Software • A free product, TMSK, was used for much of the paper’s analysis • Parts of the analysis were done in widely available software packages, SPSS and S-Plus (R ) • Many of the text manipulation functions can be performed in Perl (www.perl.com) and Python (www.python.org)

30. Software used for Text Mining Perl, TMSK, S-PLUS, SPSS SPSS, SPLUS, SAS

31. Perl • Free open source programming language • www.perl.org • Used a lot for text processing • Perl for Dummies gives a good introduction

32. Perl Functions for Parsing • $TheFile ="GLClaims.txt"; • $Linelength=length($TheFile); • open(INFILE, $TheFile) or die "File not found"; • # Initialize variables • $Linecount=0; • @alllines=(); • while(<INFILE>){ • $Theline=$_; • chomp($Theline); • $Linecount = $Linecount+1; • $Linelength=length($Theline); • @Newitems = split(/ /,$Theline); • print "@Newitems \n"; • push(@alllines, [@Newitems]); • } # end while

33. References • Hoffman, P, Perl for Dummies, Wiley, 2003 • Weiss, Shalom, Indurkhya, Nitin, Zhang, Tong and Damerau, Fred, Text Mining, Springer, 2005

34. Questions?