What should Alpha and Theta be in LDA?

What should Alpha and Theta be in LDA?

For this reason, I expect that the document-topic distribution (theta) to be high (similar topics in documents),while the topic-word distribution (alpha) be high as well (topics sharing many words in common, or, words not being so exclusive for each topic).

How to calculate a topic model in LDA?

After the preprocessing, we have two corpus objects: processedCorpus, on which we calculate an LDA topic model [1]. To this end, stopwords were removed, words were stemmed and converted to lowercase letters and special characters were removed.

Which is the most important parameter in LDA?

For parameterized models such as Latent Dirichlet Allocation (LDA), the number of topics K is the most important parameter to define in advance. How an optimal K should be selected depends on various factors. If K is too small, the collection is divided into a few very general semantic contexts.

How to fit a LDA model in TextMiner?

To fit an LDA model in textmineR, use the FitLdaModel function. Input is a document term matrix. textmineR implements 2 methods for LDA, Gibbs sampling, and variational expectation maximization (also known as variational Bayes). The default is Gibbs sampling.

Do you need to know the K number for LDA?

A lot of the following work is based on Martin Ponweiser’s thesis, Latent Dirichlet Allocation in R. One aspect of LDA, is you need to know the k number of optimal topics for the documents. From Martins work, I am using a harmonic mean method to determine k, as shown in section 4.3.3, Selection by Harmonic Mean.

How are LDA and other topic models related?

Blei (2102) states in his paper: LDA and other topic models are part of the larger field of probabilistic modeling. In generative probabilistic modeling, we treat our data as arising from a generative process that includes hidden variables.

How are hyper-parameters set in a LDA model?

I run an LDA model given by the library gensim: But I have my doubts on the specification of the hyper-parameters. From what I red in the library documentation, both hyper-parameters are set to 1/number of topics. Given that my model has 30 topics, both hyper-parameters are set to a common value 1/30.