#####################################################################
# SOURCE: http://www.r-bloggers.com/a-link-between-topicmodels-lda-and-ldavis/
#' Output topic model results in json format
#'
#' @param fitted a topic model
#' @param corpus a corpus
#' @param doc_term a document term matrix
#' @export
#' @import servr
#' @import dplyr
#' @import stringi
#' @import tm
#' @import LDAvis
topicmodels_json_ldavis <- function(fitted, corpus, doc_term){
# Required packages
library(topicmodels)
library(dplyr)
library(stringi)
library(tm)
library(LDAvis)
# Find required quantities
phi <- posterior(fitted)$terms %>% as.matrix
theta <- posterior(fitted)$topics %>% as.matrix
vocab <- colnames(phi)
doc_length <- vector()
for (i in 1:length(corpus)) {
temp <- paste(corpus[[i]]$content, collapse = ' ')
doc_length <- c(doc_length, stri_count(temp, regex = '\\S+'))
}
temp_frequency <- inspect(doc_term)
freq_matrix <- data.frame(ST = colnames(temp_frequency),
Freq = colSums(temp_frequency))
rm(temp_frequency)
# Convert to json
json_lda <- LDAvis::createJSON(phi = phi, theta = theta,
vocab = vocab,
doc.length = doc_length,
term.frequency = freq_matrix$Freq)
return(json_lda)
}
topicmodels_tf <- function(fitted, corpus, doc_term){
# Required packages
library(topicmodels)
library(dplyr)
library(stringi)
library(tm)
library(LDAvis)
# Find required quantities
phi <- posterior(fitted)$terms %>% as.matrix
theta <- posterior(fitted)$topics %>% as.matrix
vocab <- colnames(phi)
doc_length <- vector()
for (i in 1:length(corpus)) {
temp <- paste(corpus[[i]]$content, collapse = ' ')
doc_length <- c(doc_length, stri_count(temp, regex = '\\S+'))
}
temp_frequency <- inspect(doc_term)
freq_matrix <- data.frame(ST = colnames(temp_frequency),
Freq = colSums(temp_frequency))
rm(temp_frequency)
term.frequency = freq_matrix$Freq
return(term.frequency)
}
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