TL;DR: context is important.

A word cloud containing the words 'why we don't use word clouds'

Word clouds are one of those weird internet phenomenons (phenomena?) that pop up again just when you think they’ve finally gone away for good, a bit like a very long and boring game of Whack-a-Mole. They’re innocuous on the surface; it’s mildly entertaining to look at your most-used words on your blog/social media profile. (I did this once on my Facebook. The top two were ‘I’ and ‘me’.)

When used as a data analysis tool, however, a word cloud is more of a hindrance than a help. The main reason here being context.

Context matters

Say you’re running a consultation about a zebra crossing. Some of the common words you’d expect to find in an associated word cloud might be ‘safe’, ‘pedestrian’, and so on. It would be easy to glance at your word cloud, see that the big word in the middle was ‘safe’, and assume that a high proportion of road users feel safe using the zebra crossing. But if you were to look below the surface, you might actually find that a lot of responses said things like ‘the positioning of this crossing isn’t safe for the local schoolchildren to use’ or ‘I don’t feel safe using this crossing at night as it isn’t clearly marked’. It’s actually pretty much impossible to deduce the meaning of a qualitative data article from just one word.

There isn’t a good way to provide ‘at-a-glance’ summaries of qualitative data – but that’s the point of qualitative vs. quantitative. It’s a deeper, more nuanced insight.

Basically, word clouds create work by making the user dig for context. We are hostile to needless work; that goes for customers as well as staff. So we don’t build them into our tools.

Analysis tools for qualitative data in Citizen Space include tagging, wherein an analyst reads a qualitative response and then assigns it one or more categories. This is far more useful than a word cloud as a) a human has read it and understands the context and b) it groups responses together much more intelligently into categories that the analyst or organisation (rather than a computer) has decided are useful. You can read more about tagging on our knowledge base.

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