remap.terms: This function replaces instances of specified terms with...

In kshirley/LDAtools: Tools to fit a topic model using Latent Dirichlet Allocation (LDA)

Description

After tokenization, use this function to replace all occurrences of a given term with a new term, whether the new term is in the existing vocabulary or not.

Usage

remap.terms(term.map = data.frame(), term.id = integer(),
  vocab = character())

Arguments

term.map	data.frame with two columns, the first of which contains the terms to be replaced, and the second of which contains their replacements. If a replacement is not in the current vocabulary, it will be added to the vocabulary.
term.id	an integer vector containing the term ID number of every token in the corpus. Should take values between 1 and W, where W is the number of terms in the vocabulary.
vocab	a character vector of length W, containing the terms in the vocabulary. This vector must align with term.id, such that a term.id of 1 indicates the first element of vocab, a term.id of 2 indicates the second element of vocab, etc.

Value

Returns a list of length two. The first element, new.vocab, is a character vector containing the new vocabulary. The second element, new.term.id is the new vector of term ID numbers for all tokens in the data, taking integer values from 1 to the length of the new vocabulary.

kshirley/LDAtools documentation built on May 20, 2019, 7:03 p.m.