| FitLsaModel {textmineR} | R Documentation |
A wrapper for RSpectra::svds that returns
a nicely-formatted latent semantic analysis topic model.
FitLsaModel(dtm, k, calc_coherence = TRUE, return_all = FALSE, ...)
dtm |
A document term matrix of class |
k |
Number of topics |
calc_coherence |
Do you want to calculate probabilistic coherence of topics
after the model is trained? Defaults to |
return_all |
Should all objects returned from |
... |
Other arguments to pass to |
Latent semantic analysis, LSA, uses single value decomposition to factor the document term matrix. In many LSA applications, TF-IDF weights are applied to the DTM before model fitting. However, this is not strictly necessary.
Returns a list with a minimum of three objects: phi,
theta, and sv. The rows of phi index topics and the
columns index tokens. The rows of theta index documents and the
columns index topics. sv is a vector of singular values.
# Load a pre-formatted dtm data(nih_sample_dtm) # Convert raw word counts to TF-IDF frequency weights idf <- log(nrow(nih_sample_dtm) / Matrix::colSums(nih_sample_dtm > 0)) dtm_tfidf <- Matrix::t(nih_sample_dtm) * idf dtm_tfidf <- Matrix::t(dtm_tfidf) # Fit an LSA model model <- FitLsaModel(dtm = dtm_tfidf, k = 5) str(model)