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 colors, font, and exclude common or similar words.
Qualitative database from Sisters of the Road - made with Wordle
Here is an example word cloud created using Wordle. 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 option (tips and traps)
Word clouds are an easy to use and inexpensive option 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 http://www.niemanlab.org/2011/10/word-clouds-considered-harmful/
Advice for USING this option (tips and traps)
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 key words 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
Word Cloud Generator Guide: this guide provides a clear and concise guide to using Word Cloud Generator
12 valuable Wordle tips you must read: great tips on working with word clouds, including how to save the file and how to construct compound words
Wordle: this tool is easy to use and creates word clouds in a variety of shapes and colours
Tagxedo: this tool can be used to create word clouds direct from web pages, twitter feeds, search results and RSS feeds.
Tagcrowd: TagCrowd allows you to create word clouds in three different ways: by pasting the text of a document into the tool; by adding the url of a webpage; or by uploading a file to the tool.
Word Clouds could be Considered Harmful: careful consideration of the argument against word clouds.
Other ways to analyse a text
Connecting key words in a text using lines to show linkages.
Feinberg, J. (n.d.). Wordle - Beautiful Word Clouds. Retrieved August 2013, from http://www.wordle.net/
Tagxedo - Word Cloud with Styles. (n.d.). Retrieved August 2013, from http://www.tagxedo.com/