LDA: Create a Latent Dirichlet Allocation model
| LDA | R Documentation |
Create a Latent Dirichlet Allocation model
Description
This function initialize a Latent Dirichlet Allocation model.
Usage
LDA(x, K = 5, alpha = 1, beta = 0.01)
Arguments
x |
tokens object containing the texts. A coercion will be attempted if |
K |
the number of topics |
alpha |
the hyperparameter of topic-document distribution |
beta |
the hyperparameter of vocabulary distribution |
Details
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.
Value
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.
-
tokensis the tokens object used to create the model -
vocabularycontains the set of words of the corpus -
ittracks the number of Gibbs sampling iterations -
zais the list of topic assignment, aligned to thetokensobject with padding removed -
logLikelihoodreturns 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.
Author(s)
Olivier Delmarcelle
References
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
See Also
Fitting a model: fit(), extracting
top words: topWords()
Other topic models:
JST(),
rJST(),
sentopicmodel()
Examples
# 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)
