You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
69 lines
2.9 KiB
69 lines
2.9 KiB
#' Generates dfm from ElasticSearch output
|
|
#'
|
|
#' Generates dfm from ElasticSearch output
|
|
#' @param out The elasticizer-generated data frame
|
|
#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
|
|
#' @param text String indicating whether the "merged" field will contain the "full" text, old-style "lemmas" (will be deprecated), new-style "ud", or ud_upos combining lemmas with upos tags
|
|
#' @param clean Boolean indicating whether the results should be cleaned by removing words matching regex (see code).
|
|
#' @return A Quanteda dfm
|
|
#' @export
|
|
#' @examples
|
|
#' dfm_gen(out, words = '999')
|
|
|
|
|
|
#################################################################################################
|
|
#################################### DFM generator #############################
|
|
#################################################################################################
|
|
|
|
# filter(`_source.codes.timeSpent` != -1) %>% ### Exclude Norwegian summer sample hack
|
|
|
|
dfm_gen <- function(out, words = '999', text = "lemmas", clean) {
|
|
# Create subset with just ids, codes and text
|
|
out <- out %>%
|
|
select(`_id`, matches("_source.*")) ### Keep only the id and anything belonging to the source field
|
|
fields <- length(names(out))
|
|
if (text == "lemmas" || text == 'ud' || text == 'ud_upos') {
|
|
out$merged <- unlist(mclapply(seq(1,length(out[[1]]),1),merger, out = out, text = text, clean = clean, mc.cores = detectCores()))
|
|
}
|
|
if (text == "full") {
|
|
out <- mamlr:::out_parser(out, field = '_source' , clean = clean)
|
|
}
|
|
if ('_source.codes.majorTopic' %in% colnames(out)) {
|
|
out <- out %>%
|
|
mutate(codes = case_when(
|
|
.$`_source.codes.timeSpent` == -1 ~ NA_character_,
|
|
TRUE ~ .$`_source.codes.majorTopic`
|
|
)
|
|
) %>%
|
|
mutate(junk = case_when(
|
|
.$codes == 2301 ~ 1,
|
|
.$codes == 3101 ~ 1,
|
|
.$codes == 34 ~ 1,
|
|
.$`_source.codes.timeSpent` == -1 ~ NA_real_,
|
|
TRUE ~ 0
|
|
)
|
|
) %>%
|
|
mutate(aggregate = .$codes %>%
|
|
str_pad(4, side="right", pad="a") %>%
|
|
str_match("([0-9]{1,2})?[0|a][1-9|a]") %>%
|
|
.[,2] %>%
|
|
as.numeric()
|
|
)
|
|
vardoc <- out[,-seq(1,(length(names(out))-3),1)]
|
|
} else {
|
|
vardoc <- NULL
|
|
}
|
|
if (words != "999") {
|
|
### Former word count regex, includes words up until the next sentence boundary, instead of cutting to the last sentence boundary
|
|
# out$merged2 <- str_extract(lemmas, str_c("^(([\\s\\S]*? ){0,",words,"}[\\s\\S]*?[.!?])\\s+?"))
|
|
out <- out %>% rowwise() %>% mutate(merged = paste0(str_split(merged, '\\s')[[1]][1:words], collapse = ' '))
|
|
|
|
if(text != 'ud_upos') {
|
|
out$merged <- str_extract(out$merged,'.*[.?!]')
|
|
}
|
|
}
|
|
dfm <- corpus(out$merged, docnames = out$`_id`, docvars = vardoc) %>%
|
|
dfm(tolower = T, stem = F, remove_punct = T, valuetype = "regex", ngrams = 1)
|
|
return(dfm)
|
|
}
|