topic-modeling | Topic modeling utilities

I want to use GSDMM to assign topics to some tweets in my data set. The only examples I found (1 and 2) are not detailed enough. I was wondering if you know of a source (or care enough to make a small example) that shows how GSDMM is implemented using python.

I have 30 text files so far which all have multiple lines. I want to apply a LDA Model based on this tutorial . So, for me it should look this:

I created a Gensim LDA Model as shown in this tutorial: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/

I have a follow-up question to the one asked here: Mallet topic modeling - topic keys output parameter

I hope I can still get a more detailed explanation of this subject because I have trouble understanding these numbers in the output files.

What can the summation of the output numbers tell us? For example, with 20 topics and an optimization value 20 on 2000 iterations, the summation of the output is approximately 2. With the same corpus, but with 15 topics/1000 iterations/optimization 10 the result is 0,77 and with 10 topics/1000 iterations/optimization 10 it's 0,72. What does this mean? Does it even mean anything?

Also, these people are referring to these results as parameters, but for my understanding, the parameter is the optimization interval and not the result in the output. So what is the correct way to refer to the result in the output? Frequency of the topic? Is it a procentage of something? What part did I get wrong?

I would like to build an application to foreign languages learning, something based on creating decks with flashcards, words ordering, add voice (pronounciation) samples, add images etc. (something similar to ANKI app).

I want to use C# language and .NET platform to make it. I wish make both desktop and mobile app (maybe also website, but this is to be considered). And my question is what exactly technologies (type of projects) should I use to do it? It means I should make desktop app using .net framework with WPF and Xamarin to mobile app and then somehow synchronize it? I would like to synchronize desktop and mobile apps together - if someone uses desktop version and then install mobile app then he/she could have access to the whole his/her previous decks/settings and so on, maybe should I consider creating accounts? But I also wonder about the .net core and thanks to that I would make my app to be cross-platfrom.

I don't know how to correctly plan it ... Another thing is I would like to use some python libaries in that, exactly I mean gensim and generally topic-modeling, maybe also some other tools to neural networks. In general I would like to embed python things in my C# code.

Sumarizing, the aim is to create both desktop and mobile app using .net (.net framework or .net core), somehow makes the synchronization/connection between them and then (like a additional feature) use some python topic-modelling and neural network tools to enrich this whole app with some things. Would you recommend me something, some way to do it properly (some good approach to the topic), where should I start? Till now I was using mainly wpf/win forms, also entity framework, ado.net and so on. I have never used .net core but I think it's time to get to know that - it's future I suppose :)

I'm using the mallet topic-modeling tool and have some difficulties to make it stable (the topics that I get are not seemed very logic).

I worked with your tutorial and that one: https://programminghistorian.org/en/lessons/topic-modeling-and-mallet#getting-your-own-texts-into-mallet and I got some questions on that:

1. Is there some best practices for get that model to work? Except the optimize command (what is a good number for that)? What is good number for iterations command?
2. I import my data with the import dir command. In that dir there are my files. Is it matter if those files contain a text with new lines or just a very long line?
3. I read about the hLDA model. When I tried to run it I saw that the only output is the state.txt output that is not very clear. I expect for an output like the topic-modeling model (topic_keys.txt, doc_topics.txt) how can I get them?
4. When should I use the hLDA rather then the topic-modeling?

Thanks a lot for your help!