textmodel_lss: Fit a Latent Semantic Scaling model

In LSX: Semi-Supervised Algorithm for Document Scaling

Fit a Latent Semantic Scaling model

Description

Latent Semantic Scaling (LSS) is a word embedding-based semisupervised algorithm for document scaling.

Usage

textmodel_lss(x, ...)

## S3 method for class 'dfm'
textmodel_lss(
  x,
  seeds,
  terms = NULL,
  k = 300,
  slice = NULL,
  weight = "count",
  cache = FALSE,
  simil_method = "cosine",
  engine = c("RSpectra", "irlba", "rsvd"),
  auto_weight = FALSE,
  include_data = FALSE,
  group_data = FALSE,
  verbose = FALSE,
  ...
)

## S3 method for class 'fcm'
textmodel_lss(
  x,
  seeds,
  terms = NULL,
  w = 50,
  max_count = 10,
  weight = "count",
  cache = FALSE,
  simil_method = "cosine",
  engine = c("rsparse"),
  auto_weight = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x	a dfm or fcm created by quanteda::dfm() or quanteda::fcm()
...	additional arguments passed to the underlying engine.
seeds	a character vector or named numeric vector that contains seed words. If seed words contain "*", they are interpreted as glob patterns. See quanteda::valuetype.
terms	a character vector or named numeric vector that specify words for which polarity scores will be computed; if a numeric vector, words' polarity scores will be weighted accordingly; if NULL, all the features of quanteda::dfm() or quanteda::fcm() will be used.
k	the number of singular values requested to the SVD engine. Only used when x is a dfm.
slice	a number or indices of the components of word vectors used to compute similarity; slice < k to further truncate word vectors; useful for diagnosys and simulation.
weight	weighting scheme passed to quanteda::dfm_weight(). Ignored when engine is "rsparse".
cache	if TRUE, save result of SVD for next execution with identical x and settings. Use the base::options(lss_cache_dir) to change the location cache files to be save.
simil_method	specifies method to compute similarity between features. The value is passed to quanteda.textstats::textstat_simil(), "cosine" is used otherwise.
engine	select the engine to factorize x to generate word vectors. Choose from RSpectra::svds(), irlba::irlba(), rsvd::rsvd(), and rsparse::GloVe().
auto_weight	automatically determine weights to approximate the polarity of terms to seed words. See details.
include_data	if TRUE, fitted model includes the dfm supplied as x.
group_data	if TRUE, apply dfm_group(x) before saving the dfm.
verbose	show messages if TRUE.
w	the size of word vectors. Used only when x is a fcm.
max_count	passed to x_max in rsparse::GloVe$new() where cooccurrence counts are ceiled to this threshold. It should be changed according to the size of the corpus. Used only when x is a fcm.

Details

Latent Semantic Scaling (LSS) is a semisupervised document scaling method. textmodel_lss() constructs word vectors from use-provided documents (x) and weights words (terms) based on their semantic proximity to seed words (seeds). Seed words are any known polarity words (e.g. sentiment words) that users should manually choose. The required number of seed words are usually 5 to 10 for each end of the scale.

If seeds is a named numeric vector with positive and negative values, a bipolar LSS model is construct; if seeds is a character vector, a unipolar LSS model. Usually bipolar models perform better in document scaling because both ends of the scale are defined by the user.

A seed word's polarity score computed by textmodel_lss() tends to diverge from its original score given by the user because it's score is affected not only by its original score but also by the original scores of all other seed words. If auto_weight = TRUE, the original scores are weighted automatically using stats::optim() to minimize the squared difference between seed words' computed and original scores. Weighted scores are saved in seed_weighted in the object.

Please visit the package website for examples.

References

Watanabe, Kohei. 2020. "Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages", Communication Methods and Measures. doi: 10.1080/19312458.2020.1832976.

Watanabe, Kohei. 2017. "Measuring News Bias: Russia's Official News Agency ITAR-TASS' Coverage of the Ukraine Crisis" European Journal of Communication. doi: 10.1177/0267323117695735.

LSX documentation built on March 7, 2023, 6:46 p.m.