Why Are Word Clouds Inefficient and How to Improve Them?

A word cloud (also known as a tag cloud) is a collection of the most frequently occurring words in text data displayed as larger and bolder words of varying sizes. The frequency of the word determines how big it is. With the help of a word cloud generator, you can quickly generate a word cloud. They are used to get insights in a more interactive manner. It can also be used to present an overview of any topic.

They work in a simple manner, but if not used properly, they might have a negative impact. In this blog, we'll look at why word clouds are inefficient and unreliable, but first, let's go over their history.

History of the Word Cloud

According to the book Introduction to Text Visualisation, Stanley Milgram created the first word cloud visualization in 1976. He is a social psychologist who asked people to name Paris landmarks. He then constructed a map with the names of the landmarks as text. He determined the text size of each landmark based on the number of responses it received; the more responses, the bigger the size.

The "subconscious files" in Douglas Coupland's Microserfs in 1995 was the first printed example of a weighted list of English keywords, which popularised the use of word cloud on geographic maps to reflect the relative size of cities in terms of typeface size.

However, it wasn't until 2006 that word clouds became popular. The word cloud appeared on Flickr, a popular photo-sharing website. The word cloud became popular in the UX community and started to appear all over the internet. It was also popularised at the same time by Del.icio.us and Technorati (no longer exist).

Here are the reference links:

1. Tag Cloud: Think Design
2. Word Clouds Are Lame: Medium
3. Tag Cloud: Wikipedia

Why Are Word Clouds Inefficient?

Here are some of the reasons why word clouds are inefficient:

1. Word clouds can sometimes be misleading since they typically focus on analyzing individual words at a time. This means that the broader context of the words may be lost.
2. The ranking system used in the word cloud is based on the frequency of the word. The more frequently a word is used, the larger it appears in the visualization. This method, however, presents difficulties in determining the relative popularity of words beyond the first position. Because of how it is displayed, it is difficult to tell which word is the second, third, or fourth most popular.
3. This brings us to the third point, which is that it prefers longer words over shorter terms, regardless of how relevant they are to the context.
4. The arrangement of words in a word cloud is such that they are arranged in random positions, which might lead to confusion and complexity.
5. It might be difficult to determine what a word means without knowing its context and how it was used, which results in making uninformed decisions.
6. Individual words may lose meaning in the absence of context.
7. Word clouds use different words that share a common meaning within a given text, which might lead to misinterpretation.
8. Individual perspectives and levels of understanding can all have an impact on decoding the meaning of a word cloud.
9. Word cloud generators often randomly assign colors to words from a palette, which can lead to confusion.
10. Word cloud may not always provide informative insights. Typically, they consist of a combination of frequently used and obvious words, which can make them less insightful.

How to Improve Them?

One of the most effective methods is to enhance a word cloud by incorporating additional data and context into it.

For instance, if a word cloud is derived from a collection of user feedback keywords, it can be enriched by including additional data points such as user sentiment, feedback category, and others.

This is exactly what we do at Olvy.

We begin by collecting all user feedback and bringing it to Olvy with as much data as possible, including user details. Then, we use our AI stack to understand how they feel about it and get the user sentiment out.

After collecting user sentiment data, we extract the most frequently used keywords and group similar ones together (e.g., "auto tag" and "auto-tagging"). Finally, we utilize this data to build an efficient word cloud.

Conclusion

Although word cloud helps with data visualization, it is important to be aware of its limitations. Word cloud can be confusing and complex without proper context, potentially leading to misunderstandings. It is difficult to determine the meaning of the word without knowing its context and how it was used.

However, you can successfully take advantage of the benefits of word cloud by understanding its drawbacks and implementing ways to improve them. It is important to keep these things in mind to create an efficient and impactful word cloud.