Obtains predictions of topics for new documents from a fitted LSA model
# S3 method for lsa_topic_model
predict(object, newdata, ...)a fitted object of class "lsa_topic_model"
a DTM or TCM of class dgCMatrix or a numeric vector
further arguments passed to or from other methods.
a "theta" matrix with one row per document and one column per topic
# 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 on the first 50 documents
model <- FitLsaModel(dtm = dtm_tfidf[1:50,], k = 5)
# Get predictions on the next 50 documents
pred <- predict(model, dtm_tfidf[51:100,])