topic_models | implemented : lsa , plsa , lda | Topic Modeling library

It could help answerers if you included more of the information around the error message, such as the multiple-lines of call-frames that will clearly indicate which line of your code triggered the error.

However, if you receive the error KeyError: u"word 'business' not in vocabulary", you can trust that your Word2Vec instance, w2v_model, never learned the word 'business'.

This might be because it didn't appear in the training data the model was presented, or perhaps appeared but fewer than min_count times.

As you don't show the type/contents of your raw_documents variable, or code for your TokenGenerator class, it's not clear why this would have gone wrong – but those are the places to look. Double-check that raw_documents has the right contents, and that individual items inside the docgen iterable-object look like the right sort of input for Word2Vec.

Each item in the docgen iterable object should be a list-of-string-tokens, not plain strings or anything else. And, the docgen iterable must be possible of being iterated-over multiple times. For example, if you execute the following two lines, you should see the same two lists-of-string tokens (looking something like ['hello', 'world']: