alternativelywe can look at the least talked about topicwords model show_topic(counts argmin() the least talked about are the former french colonies in central africa just percent of documents touch upon itand it represents percent of the words probably if we had performed this exercise using the french wikipediawe would have obtained very different result choosing the number of topics so farwe have used fixed number of topicswhich is this was purely an arbitrary numberwe could have just as well done or topics fortunatelyfor many usersthis number does not really matter if you are going to only use the topics as an intermediate step as we did previouslythe final behavior of the system is rarely very sensitive to the exact number of topics this means that as long as you use enough topicswhether you use topics or the recommendations that result from the process will not be very different one hundred is often good number (while is too few for general collection of text documentsthe same is true of setting the alpha (avalue while playing around with it can change the topicsthe final results are again robust against this change topic modeling is often an end towards goal in that caseit 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