I have one corpus of documents on diabetes, another on Leonardo da Vinci, and another on animation and computer graphics. I combined all of these documents into a LDA and got a topic like the one below. I'm listing the top 30 terms, in descending order.
TOPIC 83: ['produced', 'company', 'john', 'weight', 'motion', 'information', 'painting', 'original', 'training', 'people', 'health', 'life', 'jesus', 'feature', 'body', 'lucretia', 'computer', 'graphics', 'time', 'madonna', 'story', 'florence', 'type', 'animated', 'paul', 'diabetes', 'animation', 'exercise', 'peter', 'film']
Many of these words do not co-occur with each other in any documents. For instance, 'lucretia' does not co-occur with 'exercise'. Yet how are these terms put together in the same topic, in the top 30 words? (30 out of 20K words or so)
Perhaps it has to do with my particular implementation? I'm using the gensim library for Python. Or is it a flaw of LDA generally?