seededlda (version 0.6.0)

textmodel_lda: Semisupervised Latent Dirichlet allocation

Description

textmodel_seededlda() implements semisupervised Latent Dirichlet allocation (seeded-LDA). The estimator's code adopted from the GibbsLDA++ library (Xuan-Hieu Phan, 2007). textmodel_seededlda() allows identification of pre-defined topics by semisupervised learning with a seed word dictionary.

Usage

textmodel_lda(
  x,
  k = 10,
  max_iter = 2000,
  alpha = NULL,
  beta = NULL,
  verbose = quanteda_options("verbose")
)
textmodel_seededlda(
  x,
  dictionary,
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  residual = FALSE,
  weight = 0.01,
  max_iter = 2000,
  alpha = NULL,
  beta = NULL,
  ...,
  verbose = quanteda_options("verbose")
)

Arguments

x

the dfm on which the model will be fit

k

the number of topics

max_iter

the maximum number of iteration in Gibbs sampling.

alpha

the hyper parameter for topic-document distribution

beta

the hyper parameter for topic-word distribution

verbose

logical; if TRUE print diagnostic information during fitting.

dictionary

a quanteda::dictionary() with seed words that define topics.

valuetype

see quanteda::valuetype

case_insensitive

see quanteda::valuetype

residual

if TRUE a residual topic (or "garbage topic") will be added to user-defined topics.

weight

pseudo count given to seed words as a proportion of total number of words in x.

...

passed to quanteda::dfm_trim to restrict seed words based on their term or document frequency. This is useful when glob patterns in the dictionary match too many words.

References

Lu, Bin et al. (2011). "Multi-aspect Sentiment Analysis with Topic Models". doi:10.5555/2117693.2119585. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops.

Watanabe, Kohei & Zhou, Yuan (2020). "Theory-Driven Analysis of Large Corpora: Semisupervised Topic Classification of the UN Speeches". doi:10.1177/0894439320907027. Social Science Computer Review.

See Also

topicmodels

Examples

# NOT RUN {
require(quanteda)

data("data_corpus_moviereviews", package = "quanteda.textmodels")
corp <- head(data_corpus_moviereviews, 500)
toks <- tokens(corp, remove_punct = TRUE, remove_symbols = TRUE, remove_number = TRUE)
dfmt <- dfm(toks) %>%
    dfm_remove(stopwords('en'), min_nchar = 2) %>%
    dfm_trim(min_termfreq = 0.90, termfreq_type = "quantile",
             max_docfreq = 0.1, docfreq_type = "prop")

# unsupervised LDA
lda <- textmodel_lda(head(dfmt, 450), 6)
terms(lda)
topics(lda)
predict(lda, newdata = tail(dfmt, 50))

# semisupervised LDA
dict <- dictionary(list(people = c("family", "couple", "kids"),
                        space = c("alien", "planet", "space"),
                        moster = c("monster*", "ghost*", "zombie*"),
                        war = c("war", "soldier*", "tanks"),
                        crime = c("crime*", "murder", "killer")))
slda <- textmodel_seededlda(dfmt, dict, residual = TRUE, min_termfreq = 10)
terms(slda)
topics(slda)
# }