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Text Mining SAS-L Topics

Text Mining SAS-L Topics. Larry Hoyle, Policy Research Institute, University of Kansas. SAS-L topics. Read each weekly topic list from http://www.listserv.uga.edu/archives/sas-l.html Parse topic, HTMLdecode Strip “Re: “ /* strip variations of re: */

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Text Mining SAS-L Topics

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  1. Text Mining SAS-L Topics Larry Hoyle, Policy Research Institute, University of Kansas

  2. SAS-L topics • Read each weekly topic list from http://www.listserv.uga.edu/archives/sas-l.html • Parse topic, HTMLdecode • Strip “Re: “ /* strip variations of re: */ topicRE = prxparse('/^ *[R|r][E|e] *: *(.*)/'); if prxmatch(topicRE, topic) then do; topic = prxposn(topicRE, 1,topic); end; • Proc SQL to aggregate topic counts across weeks

  3. SAS-L 2005 • 35324 thread/topic lines in the html files • 7081 threads after merging across weeks and a little cleaning

  4. SAS-L Top Threads in Number of Messages

  5. Text Miner on the SAS-L topics

  6. Largest clusters

  7. Smaller Clusters

  8. Message Content

  9. Web scraping with tmfilter options noxwait; %macro aweek(week=0501a); x "md C:\ddrive\projects\sugs\sugi31\SASLBOF\posts\&week"; x "md C:\ddrive\projects\sugs\sugi31\SASLBOF\filteredposts\&week"; libname sugi31 'C:\ddrive\projects\sugs\sugi31\SASLBOF\datasets'; %tmfilter( dataset=sugi31.SL&week., dir=C:\ddrive\projects\sugs\sugi31\SASLBOF\posts\&week, destdir=C:\ddrive\projects\sugs\sugi31\SASLBOF\filteredPosts\&week, URL=http://listserv.uga.edu/cgi-bin/wa?A1=ind&week.%NRSTR(&L=sas-l), depth=1, links=sugi31.SL&week.L, norestrict=' ', numchars=2000) %mend aweek; %aweek(week=0501a); %aweek(week=0501b);

  10. Parse date and sender Should parse this out

  11. Using a 10% sample of message text

  12. Using a 10% sample of message text

  13. Filter out too common terms, listserv

  14. Filter out too common terms, listserv

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