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I have a LDA model with the 10 most common topics in 10K documents. Now it's just an overview of the words with corresponding probability distribution for each topic.
I was wondering if there is something available for python to visualize these topics?
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A latent Dirichlet allocation (LDA) model is a topic model which discovers underlying topics in a collection of documents and infers word probabilities in topics. You can visualize the LDA topics using word clouds by displaying words with their corresponding topic word probabilities.
PyLDAVis - Visualization tool for LDA Models
Useful article for learning Topic Modelling using different models in Python
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pyLDAvis looks reasonably good.
There's also Termite developed by Jason Chuang of Stanford.
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