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Topic Modeling
Alternatively, we can look at the least talked about topic:
>>> words = model.show_topic(counts.argmin(), 64)
The least talked about are the former French colonies in Central Africa. Just 1.5
percent of documents touch upon it, and it represents 0.08 percent of the words.
Probably if we had performed this exercise using the French Wikipedia, we would
have obtained a very different result.
Choosing the number of topics
So far, we have used a fixed number of topics, which is 100. This was purely an
arbitrary number; we could have just as well done 20 or 200 topics. Fortunately,
for many users, this number does not really matter. If you are going to only use the
topics as an intermediate step as we did previously, the final behavior of the system
is rarely very sensitive to the exact number of topics. This means that as long as
you use enough topics, whether you use 100 topics or 200, the recommendations
that result from the process will not be very different. One hundred is often a good
number (while 20 is too few for a general collection of text documents). The same
is true of setting the alpha (α) value. While playing around with it can change the
topics, the final results are again robust against this change.
Topic modeling is often an end towards a goal. In that case, it is not
always important exactly which parameters you choose. Different
numbers of topics or values for parameters such as alpha will result in
systems whose end results are almost identical.
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