conText (version 1.4.3)

get_seq_cos_sim: Calculate cosine similarities between target word and candidates words over sequenced variable using ALC embedding approach

Description

Calculate cosine similarities between target word and candidates words over sequenced variable using ALC embedding approach

Usage

get_seq_cos_sim(
  x,
  seqvar,
  target,
  candidates,
  pre_trained,
  transform_matrix,
  window = 6,
  valuetype = "fixed",
  case_insensitive = TRUE,
  hard_cut = FALSE,
  verbose = TRUE
)

Value

a data.frame with one column for each candidate term with corresponding cosine similarity values and one column for seqvar.

Arguments

x

(character) vector - this is the set of documents (corpus) of interest

seqvar

ordered variable such as list of dates or ordered iseology scores

target

(character) vector - target word

candidates

(character) vector of features of interest

pre_trained

(numeric) a F x D matrix corresponding to pretrained embeddings. F = number of features and D = embedding dimensions. rownames(pre_trained) = set of features for which there is a pre-trained embedding.

transform_matrix

(numeric) a D x D 'a la carte' transformation matrix. D = dimensions of pretrained embeddings.

window

(numeric) - defines the size of a context (words around the target).

valuetype

the type of pattern matching: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.

case_insensitive

logical; if TRUE, ignore case when matching a pattern or dictionary values

hard_cut

(logical) - if TRUE then a context must have window x 2 tokens, if FALSE it can have window x 2 or fewer (e.g. if a doc begins with a target word, then context will have window tokens rather than window x 2)

verbose

(logical) - if TRUE, report the total number of target instances found.

Examples

library(quanteda)

# gen sequence var (here: year)
docvars(cr_sample_corpus, 'year') <- rep(2011:2014, each = 50)
cos_simsdf <- get_seq_cos_sim(x = cr_sample_corpus,
seqvar = docvars(cr_sample_corpus, 'year'),
target = "equal",
candidates = c("immigration", "immigrants"),
pre_trained = cr_glove_subset,
transform_matrix = cr_transform)