As we prepare to herald in the new year, let us take a moment to consider the recent, rapid popularisation of data visualisations on the web. Whether you attribute this to some kind of biological adaptation to visual processing, short attention spans, another web fad, or any other multitude of reasons, one thing seems to be clear - that there is a great deal of the visualisation terrain yet to be explored.
From quantitative to qualitative; simple charts to complex, interactive layouts; few tools seem to have garnered the levels of fame (or notoriety, depending on who you talk to), as the innocuous word cloud. With humble beginnings as frequency-weighted lists of metadata information, services such as Wordle that operate on free-form text have elevated their ubiquity to new heights. Art, qualitative analysis tool, learning apparatus or just pretty filler? Some would argue the latter. Here at Text is Beautiful we hold a slightly more nuanced view.
Just like any tool there is the potential for abuse and misuse, but let us delve a little deeper...
So what is good about word clouds?
- Accessibility - Free, easy to use data visualisation tools encourage people to explore and experiment with information. There may be misteps in the beginning, but we need a starting point from which to improve and evolve our processes.
- Engagement - As a pre-reading or pre-writing tool, word clouds can engage and focus our attention within a certain domain, or even potentiate the creative process through visual stimulus.
- Affirmation - Word clouds can be used to summarise and visually reinforce ideas already covered through written text.
But what about the limitations?
- Lack of context - Most word clouds only manage to utilise word-frequency in their presentation, which provides no way to accurately construe relationships between words without pre-existing knowledge of the data.
- Lack of depth - After any initial points of interest are found there are generally no mechanisms to continue exploring and formulating ideas about the data.
- Lack of insight - Because of these kinds of limitations it may be difficult to use these visualisations to elicit new insight from the data.
Where to go from here?
Well, we think that a good start might be extending the range of information represented by word clouds.
Enter, The Concept Cloud
At initial glance it appears to be no different to other, similar-looking word visualisations, but by examining closer, some deeper detail may be revealed:
Colours in the Concept Cloud are indicative of distinct themes. Themes themselves represent rough groupings of related concepts. In the above case this knowledge may enable us to make an intuitive leap and theorise that the names Milennium Falcon and Falcon are directly related to some kind of ship.
Exploring further and inspecting other concepts within the same theme, such as asteroid, space and laser, may give a more clearer picture of the nature of the ship in this story:
In this way we can begin to generate ideas about relationships in the data, swiftly building a platform from which to launch our manual investigations into the data. We think that's pretty cool.
Another interesting use of theme colouring is to select a colour palette that goes from dark to light. The colours will be applied to themes in order of decreasing connectedness. Simply, this means that the colour will fade out to white for the least connected, and generally least pertintent themes. The Concept Cloud below for the Wikipedia article on the Tasmanian Devil illustrates how this feature can instantly draw attention to the central, important concepts in the data.
So why not generate some Concept Clouds for your data and experiment with the colour palettes. And please don't hesitate to tell us about any interesting finds you make!
Hopefully we've provided some worthy ideas that argue for the potential in further experimentation and exploration with word clouds and other simple, qualitiative visualizations. Why not check out Part 2 where we introduce the Concept Web, which utilises its spatial layout to represent relatedness and structure.