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A journey into Text Analytics John McConnell Analytical People ASC Winchester 7th September 2013 © analytical-people 2013.

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Presentation on theme: "A journey into Text Analytics John McConnell Analytical People ASC Winchester 7th September 2013 © analytical-people 2013."— Presentation transcript:

1 A journey into Text Analytics John McConnell Analytical People ASC Winchester 7th September 2013 © analytical-people 2013

2 Contents Background & Objectives Our current view on Text Analytics – Value – Process An example application Conclusions 2

3 Background Text Analytics and Text Mining are largely synonymous Interest and execution of Text Analytics is growing – Social Media sources are largely responsible for this – And that often means “Big Data” This should lead to further improvements in technology and methodology which will benefit survey practitioners 3

4 Objectives We’ve been involved in more Text Analytics work in the last 2 years than in all previous years Our objective in this presentation is to share some of our experience and thoughts around some of the technology we have used 4

5 The Value Propositions 1.Reduce cost (and time) 2.Generating actionable insights – Improve public and commercial processes 5 *http://wp.eaagle.com/

6 Using Text Analytics to find Text Analytics software 6

7 3 Software tools 7 R Open Source Statistical Platform Command driven Rapid Miner Open Source Data Mining Workbench GUI Built on R and Weka SPSS Text Analytics for Surveys Commercial Text Analytics GUI

8 Unstructured data Structured data The Process – Highest Level

9 1. Extract2. Refine 3. Analyse Process – Level 2

10 How can we tell if we are using the right tool(s)? 10 Extract How good is the first extraction? How long to get to an acceptable extraction? Refine How easy is to refine? How easy is to capture refinements to re-use them in future? Analyse What tools exist to support the Text Analytics process? What tools exist to use the Structured Text in other analyses? How well do the tools/methods deliver on the value propositions?

11 Algorithms and Dictionaries Extract Algorithms e.g. Natural Language Processing (NLP) Dictionaries Variously called Lexicons, Resources, Libraries, etc. Are usually contextual e.g. Customer Satisfaction

12 Example Data The American Physical Society (APS) Student Survey Comments from 2009 (Base=1304) Q4.2 Comments about the best features of and what could be added or improved to the special programses for Student Members* 12 *http://www.aps.org/about/governance/committees/commemb/upload/2009-student-comments.pdf

13 The first extraction with R 13 library("tm", lib.loc="C:/Users/jmcconnell/Documents/R/win- library/3.0") APS2009df = read.csv("C:/AP/ASC/APS/APS2009Verbatims.csv", header = TRUE) text_corpus <- Corpus(VectorSource(APS2009df), readerControl = list(language = "en")) summary(text_corpus) #check what went in text_corpus <- tm_map(text_corpus, removeNumbers) text_corpus <- tm_map(text_corpus, removePunctuation) text_corpus <- tm_map(text_corpus, stripWhitespace) text_corpus <- tm_map(text_corpus, tolower) We apply a basic set of text handling methods (simple NLP) e.g. removePunctuation We also apply a small dictionary of known “Stopwords” (not shown)

14 R Extraction Results – Top 20 Terms 14

15 The first extraction with Rapid Miner 15 We visually construct a similar set of steps

16 Rapid Miner Extraction Results – Top 20 Terms 16

17 Improving and creating new data Refine Improve the extraction Correct mistakes Add omissions Map the extraction to structured data Group and combine meaningful terms that will become data for further analysis In second and subsequent waves (where applicable) Refine should be a shorter step where we look for new concepts

18 Rapid Miner - Refine 18 We add one process step to fix up some of the issues in the first extraction Filter Tokens sets a lower limit for the length of an extracted term/attribute

19 Rapid Miner results after first refinement 19

20 The first extraction with SPSS 20 SPSS Uses a Wizard to specify the extraction steps

21 SPSS Extraction Results – Top 20 Terms 21 Synonyms are used from the dictionaries SPSS Is counting respondents not occurrences

22 Synonyms for “Excellent” stars, 10/10, 100 % correct, 100% accurate, 100% correct, 100% grade a, 5 star, 5 stars, 5-star, ^ best $, ^ great $, a must, a nice plus, a plus, a+, a++, aagood, above and beyond, above excellence, absolute life saver, absolute word class, acceptional, admirable, all was well, allright, alright, always a please, amazing, among the best, among the very best, appreciable, appreciative, award winning, awesome, awesopme, awsome, beenfantastic, best asset, best of all, best possible, beyond expectation, beyond expectations, big asset, big beast, big hit, big hits, big kudos to, big plus, blow all others away, blows all others away, blows the doors off, brilliant $, can not be beat, can't be beat, can't beat, cannot be beat, capable, capible, class service, compliment, compliment one another well, congrats, congratulations, copious, cutting edge, cutting-edge, dandy, delight, deluxe, deserves a raise, deserves credit, does that well, doing her best, doing his best, doing their best, done very well, dynamite, exccellent, excelent, excellant, excellence, excellet, excelllent, excepional, exceptional, exceptionl, execellent, exelant, exelent, exellant, exellecent, exellent, exlt, expectional, exquis, exquise, exquises, exquisite, exquisitely $, extraordinary, extrodinary, fabulous, fairly well, fanatstic, fantabulous, fantasic, fantastic, fantatic, finest, first class, first-class, first-rate, five stars, formidable, frantastic, given me the most, godsend, goes over well, goodd, gooood, graet, grat, grea, greaat, great pleasure, greate, greatest, greeeeeeeaaattttt, gret, greta, hats down, hats off, head and shoulders better, heavenly, high hats off, ideal, impecable, impeccable, impress, impresses me most, impressive, in an orderly fashion, incomparable, incredibe, incredible, increible, indisputable, ingenious, inpecable, invaluable, is still the best, it was a pleasure, knock socks off, knock spots off, kudos, kudos to, laudable, lifesaver, made an impression, made the difference, magnificent, marvellous, marvelous, my compliments to, nicest, number 1, number one, oustanding, out of the woods, out of the world, out of this world, outperform, outperforming, outsanding, outstanding, peachy, perfect, perfection, perfectly done, phenomenal, phenominal, pleasure of working with, prettier, pretty good, quintessential, reach a ten, real good, real nice, remarkable, right direction, rock $, rocked my world, second to none, sensational, smashing, spectacular, spendid, splendid, stand head & shoulders above, stand head and shoulders above, standing head & shoulders above, standing head and shoulders above, stands head & shoulders above, stands head and shoulders above, stood head & shoulders above, stood head and shoulders above, strong positive, superb, supurb, surpassed my expectations, surreal, sweetheart, ten stars, terric, terrific, terrifig, the best, the best one so far, the best thing, the highlight of, the only one that works, thebest, think highly, think very highly, to die for, top notch, top quality, top ranked, top-flight, top-notch, top-of-the-line, top-ranked, top-ranking, topflight, topnotch, topranked, topranking, tremedous, tremendous, tried and proven, trmendous, turn out good, two thumbs up, unbeatable, unmatched, unmnatched, unparalleled, unquestionable, unquestionnable, unsurpassed, up 2 standard, up 2 standards, up 2 usual standards, up to standard, up to standards, up to usual standards, up to your usual standards, up-beat, upbeat, utmost, v-good, well done, went above and beyond my expectations, woderful, womderful, wondeful, wonderful, wonderfull, wonedeful, wonederful, would be the smartest, wounderful

23 Adding Wordnet to our R (/RapidMiner) analysis 23 library("wordnet") setDict ("C:/Wordnet/WordNet-3.0/dict") synonyms("excellent", "ADJECTIVE") [1] "excellent" "fantabulous" "first-class" "splendid"

24 Analytics to aid refinement 24

25 Job … Fair 25 Students are asking for more “stuff” at the job fair

26 R Extraction Results – Top 20 Terms 26

27 Onward to analysis 27 *This is an anonymised example

28 Onward to analysis 28 R In R we are in a statistical platform already Text Analytics outputs are part of the data in the current “Workspace” For Research style charts and tables we may need to export data Rapid Miner In RM we are in a Data Mining platform already Text Analytics is part of the current process flow SPSS Text Analytics for Surveys Data needs to be exported elsewhere for Analysis To SPSS.sav, Excel or Data Collection 3. Analyse

29 A High Level Comparison 29 AttributeRRapid MinerSPSS TAfS Help & SupportLot of User Generated Content Lots of User Generated Content Paid support option Paid support UsabilityLow level coding control Visual programmingVisual UI Scalability R in itself isn’t too scalable but many scalable implementations exist e.g. Revolution, Hadoop RadoopWe experienced Issues with data sets around 100,000 cases* ExtensibilityVarious options None AutomationCan be run in batchCan run in batchNone OverallGreat for the coder. Those familiar with R The power of R with a GUI The most graphical and tuned for Generic survey types e.g. Opinions *IBM/SPSS have a Text Analytics option for Data Mining which may be more scalable – we haven’t tested yet

30 Our current conclusions Dictionaries help in the initial extraction – But it is almost inevitable you will want to extend them to get to the specificity of the study. If the study domain is very specific you can build your own dictionaries in all 3 tools. A lot of social media monitoring starts with libraries of regular expressions built from the ground up. Open Source tools like R and Rapid Miner will continue to improve with “packages” added by the R community There is no “silver bullet”. The Refine step will typically require a lot of manual input – Especially in the initial “build” phase – More is required on larger surveys But the ROI – in time and/or cost - should be clear – And the results more robust and reliable 30

31 A journey into Text Analytics Thank-you & Questions John McConnell Analytical People


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