R: Predict method for LSA topic models

predict.lsa_topic_model {textmineR} | R Documentation

## Predict method for LSA topic models

### Description

Obtains predictions of topics for new documents from a fitted LSA model

### Usage

    ## S3 method for class 'lsa_topic_model'
    predict(object, newdata, ...)

### Arguments

`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.

### Value

a "theta" matrix with one row per document and one column per topic

### Examples

    # 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,])

[Package textmineR version 3.0.5 Index]