A presentation at OOUX Happy Hour in in Atlanta, GA, USA by Rik Williams
Machine-enabled noun foraging for OOUX a a f a a a a Rik Willi ms, UX Architect, Moor ields Eye Hospit l rikwilli ms.net/t lks/m chine-noun-for ging/
a a Origin l thre d, OOUX Forum 2021
Used in two organisations, so far… 2021 2022
Structure Parts of Speech tagging Noun sources When machine foraging is most useful Practical — my work low, so far… Merits Limits f Discussion
Parts of Speech Tagging
…or ‘grammatical tagging’
“The process of marking-up a word in a text (corpus) as it corresponds to a particular part of speech, based on both its de nition and its context.” fi a Wikipedi
“Automatically nding + tagging nouns in textbased data using computational linguistic algorithms.” fi a a a Me, on PoS T gging s pplied to OOUX
a Source: https://p rts-of-speech.info/
a Source: https://p rts-of-speech.info/
Noun sources
…‘text-based data’… 🧐🤔
Recorded interviews, us bility engineering sessions nd text-rich survey d t re gre t sources of prim ry d t Tip: automatic audio transcription services are true game changers. Record in tools like Zoom/Teams, or upload recordings to Otter.ai, Trint and Dovetail etc. a a a a a a a a a a a a a a a A user rese rch interview recorded in udio tr nscription en bled a a User research data Microsoft Te ms meeting with utom tic
PDF A justly m ligned form t, but still strong source of text-b sed nouns Tip: look for domain de nitive PDFs, both from your own organisation, competitors and the world, like: • • • • annual reports user manuals factual advice for users 5-year strategies … a a a a a a a fi f a a a a a An ex mple of vi PDF single dom in de initive d t source, of type typic lly served
Social conversations Use soci l listening d t , or mine d t b se t ble exports from user forums Tip: consider partnering with a social listening agency/partner to get the social data, at the greatest scale from the best mix of sources. a a a a a a a a a a a a a a a Soci l listening d t from 1 million re l convers tions bout termin l dise se
CRMs For ge your customer rel tionship records for nouns they cite when the eng ge with you Tips: • • look for rich content about user experience and user needs, usually captured by textArea form elds. ask your CRM developer for an en bloc export of speci c data, perhaps as CSV. a a a a fi a fi a a a a a A CRM for dementi service — extr ct rich user-centred d t entries t sc le
Content inventories Well formed titles, he dings, met descriptions nd URLs re ll noun sources Tips: look beyond your own website. a a a a a a a An inventory gener ted in 10-mins for Cr te & B rrel using Scre mingFrog Spider a • use machine-built inventories for their speed, scale, accuracy and (meta)data types. I use ScreamingFrog SEO Spider. a •
Project documents P rticul rly useful so th t ‘intern l only’ d t c n be included Tip: a good start point if you’re joining, or specifying, a project at its outset. But note that these are secondary and non user verbatim sources. a a a a f a a a a a a a a f f a A speci ic tion brief for public tender process to ind/select n gency p rtner for Moor ields Eye Hospit l.
Webpages At more th n 50 billion p ges, the WWW is n inexh ustible source! Tip: consider trialling Sketch Engine which can nd and fetch textual data via its automatic corpus builder. It can compile nouns at scale via its:… • • • web search, URLs list, website download (up to 10k pages) …functions. a a a a a a a a fi a a a Look for dom in relev nt sources, like blog posts, rticles, p pers, dvice. Both from your own org nis tion, competitors or the wider web.
Noun source considerations … s with history, be critic l of your sources Validity, bias • • are you using a primary (verbatim/user), or a secondary sources (corporate/marketing)? is the source truly relevant to your research domain? Quality • is the source data well-formed, designed? Size, spread • • how large is the corpus? how many sources are you using? Internationalisation can your tagging software work beyond English (and major European languages)? a a •
When is machine noun foraging most useful?
a a a Source: H v n Nguyen’s UX Process
a a a a Source: H v n Nguyen’s UX Process, with dded OOUX with ORCA
a a a f a a a Source: Ch os-To-Order ORCA C lend r — OOUX Certi ic tion / Sophi Pr ter
a Source: ORCA workshop fr mework, ooux.com/resources/
fl Practical — my work ow, so far…
Step 1 Choose your source text / d t We’ll be working through The Dementia Guide PDF. This is a good domain de nitive, large and noun-rich source (PDF) with which to experiment. a a a fi Source: https://bit.ly/dementi -guide-pdf
Step 2 Extr ct its corpus Select all, then copy and paste into… https://parts-of-speech.info/ …and run the tagger. a a a a T gged content from The Dementi Guide PDF in https://p rts-of-speech.info/
Step 3 Downlo d the nouns Tip: in PoS Tagger, noun download is hidden behind a spanner icon at the end of your tagged text: a f a a The r w nouns in their .txt ile downlo d, ordered by where they occurred in the text (top to bottom).
Step 4a S nitise nd norm lise the nouns Tips: sort by alphabet to reveal groupings. sort by noun count to reveal occurrences. look for plurals, these might mean instances, and instances might mean Objects. • a a a a a a a a remove junk content to increase signal and decrease noise in your data. a a a a • normalise plurals so that their noun count is accurate. a • • • Norm lising plur ls to their singul r form so th t their count (8+14) is ccur te. For ex mple, the noun ‘condition’ would occur 22 times fter norm lising plur ls.
Step 4b Identify p tterns nd rese rch questions Look for… • noun instances and consider: • • • • normalising to the overarching ‘super-noun’ (proto-Object) instances as potential Metadata user roles in the system opportunities, which nouns are just intriguing/interesting? a a a a a a a a a a a a a a a a a Ex mple rese rch questions: Is ‘C rer’ n Object, Role or Both in this dom in/system? Is ‘CBT’ (Cognitive Beh viour l Ther py) n inst nce of n over rching ‘Tre tment’? 🧐 🤔
Step 5 (optional) Aggreg te nd tri ngul te Tips: use many content sources (both types and instances) so that your analysis is more robust. a a a a a a a a a a a a a Tri ngul ting nouns from m ny count sources nd ssessing their comp r tive frequency of occurrence. Source: https://bit.ly/m chine-noun-counting-templ te
Step 6 V lid te! Noun foraging is an early step in OO research and design. And your noun’s aren’t gospel. Machine-enabled noun foraging is but +1 tool in your arsenal to help make sense of messes. a a a a a a a a a a a a a a A simple system model in the ORCA OOUX process for dementi service. One w y to explore/v lid te potenti l rel tionships between objects (which were grounded in m chine-en bled noun for ging) in workshop with subject m tter experts.
Practical Resources (Google Drive) for following long, if you w nt to… https://bit.ly/oouxHH a a a c se sensitive
Merits
Fast, cheap way to see the wood (objects) for the trees (noise) in a domain / landscape
Grounded in extant data* a a a a f a a a
Can (re)use and integrate qualitative user research data towards a working structure
fi Kick starts research question / assumption de nition for followup / validation
“ OOUX is a process to get ‘project questions from the future’ so that there are fewer surprises later.” a a Sophi Pr ter
Limits
Only as good as the source data — rubbish in, rubbish out
Noun frequency is a guide, not a gospel…
…so don’t discount potential noun ‘gold’ in the long tail
Only works with text-based data* a a a a a a a a
Only works with data you already have, which could limit scope to innovate
Some people just hate spreadsheets 🤷
Can fail the ‘headphone’s on test’* — any work you can do listening to a podcast isolates you from your users and your team a a a
TMK: limited to English and major European languages
https://parts-of-speech.info/ is a single point of failure* a a a a a
Ideas
Ideas things I will be experimenting with… • • Isolating, counting and listing potential: • • • Roles Metadata Content Attributes Extracting Verbs, for Object call to actions* a f a
Feedback
“[Good] design depends on critique as an engine.” a D n Brown
https://forum.ooux.com/
rikwilliams.net/talks/machine-noun-foraging/
A cheeky plug…
a a a Join/spe k with us t: meetup.com/rese rchthing/
Discussion
Defining the right objects is critical to successful UX.
Noun foraging, the first step of the OOUX process and is a valuable activity in its own right, is all about finding those important objects — the things that our users actually care about — so that they can become the anchors of a valuable object-oriented user experience.
To make sure we are confident about our objects, we want to forage from a diverse landscape of resources.
We go foraging in user interview transcripts, competitor websites, our own existing systems, Wikipedia articles, and customer service chat logs.
However, foraging across reams of transcripts, case studies, and SME emails, can be a challenge.
This talk at Object-Oriented UX Happy Hour introduces a software-based method to mine diverse content sources.
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Talk recording, tools, resources and further reading cited in the slides/talk.
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