Conference September 2013

Text analysis software needs more common sense and less

intelligence!John S. Lemon, University of

Aberdeen1

Open Day 2013IT Services

Student Liaison OfficerJohn S. Lemon

Introduction

• History – setting the scene • Problem – move from quantitative to

qualitative• Etc.

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

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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 5

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 6

History

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

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

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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

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History

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

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

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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

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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

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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

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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

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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.

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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

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SNAP

• Not very helpful• Is there a

difference between ‘computer’ and ‘computers’ ?

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Word cloud of Free text comments

workgood

computers

computer

time

service

staff

eduroam

MyAberdeen

internet

Services

access

libraryuniversity

helpful

help

problems

slow

Campus

students

SNAP

• Not only ‘computer(s)’ presented problems• But all the different terms students use for the

wireless network.• These are the more obvious

spellings – ignoring the miss-spellings.

• Not ideal as did not allow for synonyms

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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

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Concordance

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

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NVivo

• Very powerful • Accepts data from a wide variety of sources:

– Text– Video– Pictures– Web– Social media– Etc.

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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.

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NVivo

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NVivo

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

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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

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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 problem27

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 28

STAFS

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STAFS

• Has the ability to indicate “good” and “bad” phrases in green, and red

• It also highlights the context in amber

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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

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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 32

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

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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

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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 !!

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