Cluster titles or ingredients of food into n-categories I have a dataset which has information about food recipes (in german), that looks like this: 

Here is a link to a small .csv file (first 1000 rows of my data)
https://drive.google.com/file/d/1C7thFlOnDn-oTc6AaDWA3CXXcX8m9NRu

The idea is to cluster the recipe names into n-categories so that afterwards I can assign every recipe to a category. Of note, there are tags and ingredients for every recipe, maybe this information helps to refine the clusters?
For example the algorithm (maybe a semantic analysis?) should output: categorised recipes into 200 biggest found categories: (hamburger, soup, pizza, ...)

Is there a way to do this?

Note: I have for every recipe min. 1 image. The idea is to label my images with n-categories, afterwards to train a convolutional neural network with my data.
The input would be an food image, the output would be a category.
This sounds like a job for latent dirichlet alocation (i.e. topic modeling).


https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
https://radimrehurek.com/gensim/wiki.html