![]() ![]() While I wouldn’t recommend them as a method of analysis or information extraction, they’re a useful tool for presentation - people seem to find them fun and engaging, and they’re a good thing to have on initial pages of presentations, etc. The criticisms of wordclouds are absolutely valid, but that’s not to say that wordclouds are pointless. DisclaimerĮvery statistician I have ever met requires me to inform you that wordclouds are not useful for analysis because they are simplistic and often misleading I recommend this excellent article which goes into more detail on the problems with the form. ![]() You can find a complete copy of the code for this tutorial on Github, along with the text data and images used throughout. This tutorial was written using using Jupyter notebooks, Python 3.7.5 and Wordcloud 1.6.0 things might behave slightly differently if you’re in a different IDE or using different versions of the language/library. In this tutorial, I’ll explain how to generate wordclouds using the Wordcloud library, showing how to customise and improve your visualisations. ![]() In Python, the simplest and most effective way to generate wordclouds is through the use of the Wordcloud library. Wordclouds are a quick, engaging way to visualise text data. ![]()
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