Interpreting LogLikelihood For LDA Topic Modeling

Hi,

it's the negative LLH. The lower the better.

BR,
Martin

Thanks for prompt reply, so in this case -230000 is better than -240000 or vice versa?

Hi @svtorykh,

-240000 is better.

BR,

Martin

Thanks so much Martin!

By the way, @svtorykh,

one of the next updates will have more performance measures for LDA. Just need to find time to implement it. LLH by itself is always tricky, because it naturally falls down for more topics.

BR,

Martin

That would be very nice to have! Please keep us posted Martin!

Hello. I want to find the optimal K-number for KMEANS with the LDA Loglikelihood value

For me, using alpha and beta as heuristics for the top 5 is the highest. Now, how to use K optimally. Does anyone know how to help? Thanks a lot I searched a lot, but I did not find anything:smileysad:

Hey @jozeftomas_2020,

i am fairly confused. KMeans and LDA are fairly different models. Why and how do you want to mix them?

~Martin

In the articles I have seen using the LDA to find optimal k, but I do not know how?
And how can I understand which LDA has a better result? Alpha and beta need to be adjusted a little or too high to get a better result?

I'm so sorry
Thanks a lot

@svtorykh,

i've added Perplexity as the default to the performance of LDA. Perplexity is defined as

exp(-LLH/tokens)

and should be minimized. That's somewhat what you see in common blog posts on LDA.

It's not yet on the marketplace. Let's see when we have enough features to publish.

Cheers,

Martin

Thanks much!

Always happy to help! Will it be possible that you present your use case at RM Wisdom in October?