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10 iterations: 100 iterations: Type 2 (this grid is for doc set with type id 5 from. Mostly the topics make at least some sense but many of those coherence measures show higher values for bigger numbers. So if I want to apply topic models, what would I do right now (NLP is getting lots of attention so who knows in a few years.)? If I needed to model large numbers of separate sets that are evolving over time, I might just use the cohesion metrics along with some heuristics (e.g., number of docs vs number of topics) to make automated choices, run the things as micro-services at intervals. Type 3 refers to models where there was no big difference in final topic diff in 10 vs 100 iterations. The machine I ran it on has 32GB RAM and a quad-core Core i7 processor (hyperthreads to 8 virtual cores). Tune as needed over time. Unless maybe if you want to capture really fine grained differences in topics. To see a large number of topics at once vs cycling through one at a time. Seems reasonable given the smallish number of documents I have. So I plotted the topic diff for the wikipedia run (when generating the LDA models to see how much the topics drift during the run. Maybe somewhat equal to iterations of old.
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Implemented LDA in Java back then based on that tutorial. Both for default parameters and autotuned parameters. Unfortunately, I am not paid for this and have too many other things. Have to say, maybe not very excited. Wikipedia example: This dumps the whole LDAvis thing into a html file you can then load up any time later and play with. Code: And to plot it: And the results for each of the document sets: Doc set id 10 iterations 100 iterations So how does all this feel when I load the topics up and look at them? Buy the Full Version. Buy the Full Version, you're Reading a Free Preview, pages 225 to 230 are not shown in this preview. Not in my scope to investigate further, but the reasons could be anything, what do I know. And the Gensim docs also nicely describe how running this online algorithm now also merges the results in a way that you dont necessarily need to run large numbers of passes (iterations) over the corpus to converge on a better model. A handy tool for topic exploration. See where that takes.
So if the smaller number of topics would be better, maybe I need to try even smaller number of topics. Gensim nicely comes with a script to parse it for dictionary and corpus: python -m ke_wiki, then some code to build different sizes of topic models (25 to 200 topics in 25 topic size increments). Or maybe I am just bad at using stuff. I am sure this would also be an interesting topic to study, why PCA grounds them together.
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