I want to use Latent Dirichlet Allocation for a project and I am using Python with the gensim library. After finding the topics I would like to cluster the documents using an algorithm such as k-means(Ideally I would like to use a good one for overlapping clusters so any recommendation is welcomed). I managed to get the topics but they are in the form of:
0.041*Minister + 0.041*Key + 0.041*moments + 0.041*controversial + 0.041*Prime
In order to apply a clustering algorithm, and correct me if I'm wrong, I believe I should find a way to represent each word as a number using either tfidf or word2vec.
Do you have any ideas of how I could "strip" the textual information from e.g. a list, in order to do so and then place them back in order to make the appropriate multiplication?
For instance the way I see it if the word Minister has a tfidf weight of 0.042 and so on for any other word within the same topic I should be to compute something like:
0.041*0.42 + ... + 0.041*tfidf(Prime) and get a result that will be later on used in order to cluster the results.
Thank you for your time.