Conference September 2013

1. Conference September 2013 Text analysis software needs more common sense and less intelligence! John S. Lemon, University of Aberdeen

2. Open Day 2013 IT Services John S. Lemon Student Liaison Officer

3. Introduction • History – setting the scene • Problem – move from quantitative to qualitative • Etc.

4. Introduction • History – setting the scene • Problem – move from quantitative to qualitative • How - Analysis / reporting • Quantity – increases each year • Constraints • Reports required earlier each year • Very limited budget

5. Disclaimer • I am not a statistician – I just have to present reports • When I started at university in 1975 almost all data was numeric / quantitative • For the purposes of this paper I emulated a naive user • To carry out the analysis there is no budget for: • Software • Training

6. History • IT Services ( formerly DISS & DIT ) runs an annual survey to: • Staff • Students • Purpose is to identify satisfaction with facilities and service • Originally on paper and scanned – almost entirely tick boxes • Moved to web but retained ‘tick box’ format

7. History • Converted to WebHost around 2008/9 • Still retained the mainly quantitative original

8. History • SNAP had been used to create Student Course Evaluation Forms ( SCEF ) • On paper since 1999 – two sides of Likert scales • Only one free text box • 60,000 forms scanned / year • In 2010 deemed to be ‘not green’ / ecological • Move to special web based software • Move to free text comments

9. History • This is the 2007 paper form • As SCEF forms had changed approach it was decided the annual survey would do the same • Fewer tick boxes

10. History • From 2011 some check boxes but more free text options.

11. Problem - quantitative to qualitative • Report generation could no longer rely on • charts • tables. • No thought given to how to cope with free text • First year one person (me) • ‘skimmed’ the responses • Subdivided according to which area of service was commented on • Passed to section heads for action and responses

12. Problem - quantitative to qualitative • Second year – manual coding • Excel file of case number and free text comments • Plus extra columns for coding comments / categorisation • Code values were “Positive”, “Negative” or “Ambiguous” • Limited number of categories • Needed consistency so one person coded all

13. Problem - quantitative to qualitative • Once coded loaded into SPSS • Merged with original file • Produced tables and charts combining demographic data and coded values • Extremely labour intensive • Needed an iterative approach for accuracy • Categories were too broad or too detailed • Codes were too restrictive

14. Problem - quantitative to qualitative • This year attempted a new approach • Use software • New / updated versions of: • SNAP (11) • Nvivo (10) • STAFS - SPSS Text analysis For Surveys (4) • Also consider use of concordance software

15. Problem - quantitative to qualitative • Why choose these four products ? • SNAP • Already had so no extra cost • Had SNAP format files so no translating / transforming the data • NVivo • Like SNAP already had on site • Claims that it would meet all requirements • Takes data from many sources

16. Problem - quantitative to qualitative • Why choose these four products ? • SPSS Text Analysis For Surveys • Reads SPSS files which SNAP would create • Export coded categories back to SPSS • Being considered for site licence • Concordance • Language / literature department recommendation • Cheap • Appeared easy to use.

17. SNAP • Survey had been done in SNAP so tried first • New features are: • word ‘cloud’ • Auto coding of text / words • Can combine all the free text questions into one new ‘derived’ / auto-recoded variable

18. SNAP • Not very helpful • Is there a difference between ‘computer’ and‘computers’ ?

19. SNAP • Not only ‘computer(s)’ presented problems • But all the different terms students use for the wireless network. • These are the more obviousspellings – ignoring themiss-spellings. • Not ideal as did not allow for synonyms

20. SNAP - limitations • Has a ‘Stop’ list – words to exclude • No equivalent list to create synonyms • Would like to be able to do:{wifi,wi-fi,eduroam,resnet,wireless}={wireless} • Not just a limitation of SNAP word cloud • In the time available could not find how to export auto-coded variables to SPSS

21. Concordance • Cheaper but very limited • No ability to easily export the results • Positive point is it shows need for synonyms !!

22. NVivo • Very powerful • Accepts data from a wide variety of sources: • Text • Video • Pictures • Web • Social media • Etc.

23. NVivo • Data needed some pre-preparation before input • Some of the concepts weren’t obvious • Took a number of attempts to get the data into the correct format • It will combine terms • But may not be exactly what you want • Some of the words for ‘connect’ are quite imaginative to say the least.

24. NVivo

25. NVivo • Depending on how ‘tight’ or ‘loose’ the word associations were made could end up with entirely different results / word clouds

26. NVivo • Found difficulty in: • Trying to get the data categorised • Exporting the results to merge back to SPSS • Alternatively try and produce tables and charts linked to demographic data within NVivo • Problems with all the different software were: • Time to learn all idiosyncrasies • Impatient line managers • Nomenclature

27. STAFS • Appears to be very powerful and comprehensive • Very large manual • Like Nvivio has different nomenclature for the aspects of analysis • Will read data from SPSS files • Providing the text fields are less than 4000 characters in length • Looked the most promising to solve the problem

28. STAFS • Foolishly left it until last for evaluation • Very little time left to get to grips with yet another set of concepts • The deadline for the report was approaching so not a lot of time • Also trial version which lasted 14 days • Appears to have a bit more intelligence in matching words together

29. STAFS

30. STAFS • Has the ability to indicate “good” and “bad” phrases in green, and red • It also highlights the context inamber

31. STAFS • Problem is that the file that ‘drives’ this appears to be rather general in approach • To really be useful in future it needs tailoring • Ran out of time to really develop expertise in this • Potential to apply a level of ‘common sense’ • Not easy to actually do in the time available. • Export back to merge with SPSS appeared OK • But had to abandon any further experiments

32. What was used finally • Time for testing / experimentation had run out • Only one course of action • By hand • One person – me • Scale of problem • When loaded into Word as single spaced, normal margins, 12 pt Calibri • Just under 500 pages • A ream of paper

33. Next year • Try and get a longer trial period for STAFS • Experiment with this years data to provide coding file • Use STAFS from the start

34. Conclusion • Don’t try and learn a lot of new software when there are deadlines from “management” • Word clouds don’t help much • A concordance really only highlights speeling idiosyncrasies • Care must be taken when allowing software to make choices in coding

35. Conclusion • Does text analysis software have intelligence ? • Up to a point • Does it have common sense • Of the four tried only one does BUT • It needs teaching “common sense” and that takes time • Just like a child !!