A word cloud (or tag cloud) is a visual display of text data that represents each word (or phrase) in a  font size that is proportional to the importance of each word.  The relative importance of the words can then be easily appreciated simply by viewing the resulting chart. 

In sensory and consumer science a word cloud is most often used to visualize the associations between products and binary variables indicating the absence or presence of a range of different attributes.  Usually this process if called check-all-that-apply or CATA for short.  The attributes themselves can be any characteristic of the products, e.g. functional properties, or even feelings or emotions that might be evoked by the products.  In practice the subject is given a list of the attributes and, while evaluating the product, places a tick next to each that they feel is appropriate.  The CATA data is first summarised by counting the number of subjects who associate each attribute/word with each product, then a word cloud for one product is constructed by writing the words in a font size proportional to subject count.  The algorithm then places the different sized words into a standard area, such that there are both closely packed together, and where the words with the largest font are as close to the centre of the plot as possible.  In order to place the words closely together, words may be written either horizontally or vertically, so no particular significance is intended by the direction in which a word is written.  The word cloud is a good way of visualising the most important associations between a product and a word since the viewer’s eye is most readily drawn towards the words in the largest font size toward the centre of the chart.  Using the example data set with emotional attributes CATA emotions.xlsx it can easily be seen that Product C (the first plot below) is most strongly associated with positive emotions such as “Interested”, “Excitement” and “Approval”, while Product J (send second plot below) is most strongly associated with negative emotions such as “Bored”, “Not Excited” and “Not Interested”.