Word clouds or tag clouds are graphical representations of word frequency that give greater prominence to words that appear more frequently in a source text.
The larger the word in the visual the more common the word was in the document(s). This type of visualization can assist evaluators with exploratory textual analysis by identifying words that frequently appear in a set of interviews, documents, or other text. It can also be used for communicating the most salient points or themes in the reporting stage.
A variety of word and tag cloud generators are freely available on the internet and the process for creating them is straightforward. Evaluators can simply import text (for example, a set of interviews) into a text box and the tool creates a graphical representation of the words. Most word cloud generators have features that allow users to change colours, font, and exclude common or similar words.
Qualitative database from Sisters of the Road - made with Wordle.net
Here is an example word cloud created using Wordle.net (archived link of old website - not related to the word game prominent in 2022). The content within the word cloud is from interview transcripts of individuals discussing their experience with homelessness.
Qualitative database from Sisters of the Road - made with Tagxedo
This second example uses the same data set, but uses Tagxedo - a word cloud tool that provides the option to display the text in a shape or image.
Advice for choosing this method
Word clouds are an easy to use and inexpensive method for visualizing text data. One of the challenges of interpreting word clouds is that the display emphasizes frequency of words, not necessarily their importance.
Word clouds will not accurately reflect the content of text if slightly different words are used for the same idea (for example, ‘large’, ‘huge’, ‘giant’, ‘enormous’, and ‘big’). They also do not provide context, so the meaning of individual words may be lost. Because of these limitations, word clouds are best suited for exploratory qualitative analysis.
Careful consideration of the argument against word clouds can be found here.
Advice for using this method
Take time to clean up the data before importing it into the word cloud generator. For example, interview transcripts, may have common conversational fillers such as “you know” or “like” that need to be removed.
Ensure that the data collection team documents keywords consistently– with consistent capitalization – since some software packages will not recognize capitalized and uncapitalized words as the same.
Validate findings with participants.
If used for reporting, communicate the limitations of the visualization with the audience
Feinberg, J. (n.d.). Wordle - Beautiful Word Clouds. Retrieved August 2013, from wordle.net
Sisters Of The Road. (2002). Voices of Homelessness: a qualitative database from Sisters of the Road. Retrieved April 10, 2013, http://sistersoftheroad.org/voices/ (archived link)
Tagxedo - Word Cloud with Styles. (n.d.). Retrieved August 2013, from http://www.tagxedo.com/
'Word cloud' is referenced in:
- Rainbow Framework :