Build a faSTM corpus from prepared text

faSTM does not do its own tokenization — it reads an already-prepared document-term representation from the tools the field already uses (quanteda, tidytext) or a plain sparse matrix. as_corpus() normalizes any of these into the structure stm() consumes, dropping empty documents and re-indexing the vocabulary, with metadata kept aligned.

Usage

as_corpus(x, meta = NULL, ...)

Arguments

x

A quanteda dfm, a document-term Matrix/matrix (documents in rows, terms in columns, with colnames), or an existing faSTM_corpus. For a tidy (long) term table use from_tidy().

meta

Optional data.frame of document metadata, one row per document, aligned to x. For a dfm, defaults to quanteda::docvars(x).

...

Unused.

Value

A faSTM_corpus: a list with documents (named list of 2×n integer matrices: row 1 = 1-based term id, row 2 = count), vocab (character), meta (data.frame or NULL), and word_counts (corpus term frequencies).