topic-model | 文本的主题提取，采用LDAGibbssSample | Topic Modeling library

You want convert(). e.g.

I believe you want to select the top 10 words and you are using a wrong syntax. You are only selecting the word ranked 10 which is not iterable. Change line 261 to this to select the top 10 instead of only selecting the 10th:

GSDMM (Gibbs Sampling Dirichlet Multinomial Mixture) is a short text clustering model. It is essentially a modified LDA (Latent Drichlet Allocation) which suppose that a document such as a tweet or any other text encompasses one topic.

GSDMM

LDA

Address: github.com/da03/GSDMM

The issue is related to the way datasets are imported in the Power BI Query Editor using Python. To fix the issue, import the data via:

Loop over the files, 1 to 31 (last is skipped by the range() function:

You are looking for the package topicdoc, read the basic vignette.

You use this after you have created a set of topicmodels with the topicmodel package.

It turns out that there was nothing wrong with the call to comprehend.describe_topics_detection_job -- it was just returning, in describe_result, something that could not be json serialized, so json.dumps(describe_result)) was throwing an error.

Solved! Coherence Model requires the original text, instead of the training corpus fed to LDA_Model - so when i ran this:

You're correct that parameter is being used to mean two different things here.

• Parameters of the statistical model are values that determine the properties of that model. In this case they determine which topics we expect to occur more often, and how confident we are of that. In some cases these are set by the user, in other cases they are set by the inference algorithm.

• Parameters of the inference algorithm are settings that determine the procedure by which we set the parameters of the statistical model.

An additional confusion is that when model parameters are explicitly set by the user, Mallet uses the same interface as for algorithm settings.

The numbers you see are the parameters of a Dirichlet distribution that describes our prior expectation of the mix of topics in a document. You can think of it as having two parts: proportions and magnitude. If you rescale the numbers to add up to 1.0, the resulting proportions would tell you the model's guess at which topics occur most frequently. The actual sum of the numbers (the magnitude) tells you how confident the model is that this is the actual proportion you will see in a document. Smaller values indicate more variability.

A possible explanation for the numbers you're seeing (and please treat this as raw speculation) is that the 20 topic model has more flexibility to fit consistent topics, and so it is about three times more confident that there are topics that consistently occur more often in documents. As the number of topics decreases, the specificity of topics drops, so it is more likely that any particular topic could be large in any given document.