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This function initialize a Latent Dirichlet Allocation model.
LDA(x, K = 5, alpha = 1, beta = 0.01)An S3 list containing the model parameter and the estimated mixture.
This object corresponds to a Gibbs sampler estimator with zero iterations.
The MCMC can be iterated using the fit()
function.
tokens is the tokens object used to create the model
vocabulary contains the set of words of the corpus
it tracks the number of Gibbs sampling iterations
za is the list of topic assignment, aligned to the tokens object with
padding removed
logLikelihood returns the measured log-likelihood at each iteration,
with a breakdown of the likelihood into hierarchical components as
attribute
The topWords() function easily extract the most probables words of each
topic/sentiment.
tokens object containing the texts. A coercion will be attempted if x is not a tokens.
the number of topics
the hyperparameter of topic-document distribution
the hyperparameter of vocabulary distribution
Olivier Delmarcelle
The rJST.LDA methods enable the transition from a previously
estimated LDA model to a sentiment-aware rJST model. The function
retains the previously estimated topics and randomly assigns sentiment to
every word of the corpus. The new model will retain the iteration count of
the initial LDA model.
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993--1022.
Fitting a model: fit(), extracting
top words: topWords()
Other topic models:
JST(),
rJST(),
sentopicmodel()
# creating a model
LDA(ECB_press_conferences_tokens, K = 5, alpha = 0.1, beta = 0.01)
# estimating an LDA model
lda <- LDA(ECB_press_conferences_tokens)
lda <- fit(lda, 100)
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