One-table summary of the topics of a model

A first table to read a model: for each topic, a label made of its most characteristic words, its average prevalence, and the share of documents in which it is the main topic. Reading it from the most to the least prevalent topic is a good way to start describing a model.

Usage

topic_summary(model, n_terms = 5L, measure = "frex", ...)

Arguments

model

A fitted stm model without content covariate (see marginal_topic_model() otherwise).

n_terms

Integer scalar, number of words in the label.

measure

Measure used to pick the words, as in topic_labels().

...

Not used: an argument renamed in the development version of mintR gives an error naming its replacement.

Value

A data.table with topic, label, prevalence (mean of the topic's proportion over documents, summing to 1 over topics) and share_main (share of documents where the topic has the highest proportion), by decreasing prevalence.

Examples

if (FALSE) { # \dontrun{
topic_summary(fit)
} # }