| predict.lsa_topic_model {textmineR} | R Documentation |
Obtains predictions of topics for new documents from a fitted LSA model
## S3 method for class 'lsa_topic_model' predict(object, newdata, ...)
object |
a fitted object of class "lsa_topic_model" |
newdata |
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,])