LDA ( Latent Dirichlet allocation) - is quite a popular topic in data-mining.

Question What are the industrial systems using LDA or may be some related models ? (May be Google/Amazon/ ... ? )


I would be also thankful for comments on: https://mathoverflow.net/questions/304352/latent-dirichlet-allocation-math-words-digest

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    $\begingroup$ Do you want the names of the systems using LDA or In general Applications of LDA in industrial Systems? I can't comment on the former but I have built systems which use LDA as a starting point (Clustering of Documents) and go on to build ensembles from there. $\endgroup$ – Kaustubh Jul 6 '18 at 7:49
  • $\begingroup$ @Kaustubh Well, any information sharing is welcome, so please feel free to share any your relevant experience. My main interest would be to hear about somewhat industrial applications which are already used by many "common people", rather than research project which would be potentially used in future. But any way you are welcome to share your ideas. $\endgroup$ – Alexander Chervov Jul 6 '18 at 8:12

I can't answer for the systems in already existing companies, but I can definitely share an application of LDA in NLP.

Latent Dirichlet Allocation is a popular technique use for topic modelling in Natural Language Processing. The idea is to have corpus of natural langue text with lots of documents and the goal is to get the distribution of the words appearing in the corpus each (Distribution) being termed as a topic.

For visual analysis you can view this note book here. It makes use of the NLP library built for LDA, called pyLDAvis.

So lets say you have a corpus, and you want to build a tagger which labels the document(input text) into one of the N Classes, but you don't have labelled data. How do you start? One way is to use LDA to perform Topic Modelling, get the topics, then name the topics to N Classes. Label the data using LDA model, Use the loosely labeled data to build a classifier.

  • $\begingroup$ Thank you for your answer ! May I ask you what the application for the "end-user" we should keep in mind doing that ? $\endgroup$ – Alexander Chervov Jul 6 '18 at 9:20
  • $\begingroup$ In my case 'end-user' was my team (Data Scientist) who wanted to quickly label some data to build a classifier, our use case was to build a classifier for news articles. $\endgroup$ – Kaustubh Jul 6 '18 at 9:33

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