Analyzing_world_cuisines VS word_cloud

Source: BBC Food. Github repo. wordcloud in python.

The repo can be found here. There was a critical bug that incorrectly labeled recipes with cuisines. For those interested, the pandas python library apparently automatically does an inner merge with indexes if you add a column, as opposed to simply concatenating two dataframes.

For anyone interested I shared the process on github here. Source of data is BBC Food website, which categorizes recipes into cuisines and these categories I used. Note that not all cuisines are included, some had too few recipes to get any meaningful data from. I deliberately excluded too common ingredients (salt) and grouped others, but there are many ways to approach this. Also note these are recipes from many cuisines likely written by european authors, so take this with a grain of salt - results are only as good as the data input.

wordcloud to make the word clouds

- Logo with WordCloud. The texts used were from the plot/synopsis from the Wikipedia pages of the top 20 anime according to MyAnimeList (excluding anime from the same series. I'm looking at you Gintama).

Here's a GitHub issue that tracks this.

Word arrangement generated using the wordcloud Python package with a custom placement mask (skull design by me) and custom color function. Common English words removed before processing.

After reading this tutorial you should now be able to generate your own Wordcloud using Python. Checkout the WordCloud for Python Documentation. Use your imagination and have fun. Please leave like or comment if you found this article interesting!

If you are proficient with Python then there is also the wordcloud library (and its homepage/documentation) to make a custom word cloud for yourself rather than relying on TagCrowd. For example, I noticed that using the HasteBin as a URL source caused TagCrowd to put some gibberish words in the cloud, like “donaeurtmt”, which do not appear when simply pasting the raw text into the text field.

The project is very heavily based on https://github.com/amueller/word_cloud

A quick and easy way to get some context into anything being mentioned ensure the CSV is only the responses and run it through a word cloud generator in Python to determine what those keywords are. That should help you see what your customers are at least mentioning and then go from there to look for surrounding context.