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With freely available pretrained models like GloVe, classification has become quite easy.

But where to start if I do not know which categories are there?

In the example linked above we "suggest" categories to the classification algorithm:

# Category -> words
data = {
  'Names': ['john','jay','dan','nathan','bob'],
  'Colors': ['yellow', 'red','green'],
  'Places': ['tokyo','bejing','washington','mumbai'],
}

Please give me a hint whether and how it could be possible to derive suggestions for possible categories from a larger data set. Output could be for example:

# Category suggestions
categories_suggestions = {
  'cat1': ['fish','bird', 'snake'],
  'cat2': ['wood', 'metal','plastic'],
 ..
}

So that a human could look upon it and say, ah ok, let's name cat1 "animals" (even if "pets" could appear as more appropriate afterwards) and cat2 "materials".

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  • $\begingroup$ Please provide more detail: so will be all documenrs' words mapped to clusters? $\endgroup$ – J. Doe May 12 '19 at 11:09

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