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@ -4,7 +4,7 @@
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#' @param out The elasticizer-generated data frame
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#' @param out The elasticizer-generated data frame
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#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
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#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
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#' @param text String indicating whether the "merged" field will contain the "full" text, or "lemmas"
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#' @param text String indicating whether the "merged" field will contain the "full" text, or "lemmas"
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#' @return A Quanteda dfm
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#' @return A Quanteda dfm
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#' @export
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#' @export
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#' @examples
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#' @examples
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#' dfm_gen(out, words = '999')
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#' dfm_gen(out, words = '999')
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@ -32,7 +32,8 @@ dfm_gen <- function(out,words = '999', text = c("lemmas","full")) {
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str_replace_na(out$`_source.text`, replacement = " "),
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str_replace_na(out$`_source.text`, replacement = " "),
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sep = " ") %>%
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sep = " ") %>%
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# Remove html tags
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# Remove html tags
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str_replace_all("<.*?>", " ")
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str_replace_all("<.*?>", " ") %>%
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str_replace_all("\\s+"," ")
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}
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}
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# out$codes <- out$`_source.codes.majorTopic` %>%
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# out$codes <- out$`_source.codes.majorTopic` %>%
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out <- out %>%
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out <- out %>%
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@ -58,4 +59,4 @@ dfm_gen <- function(out,words = '999', text = c("lemmas","full")) {
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dfm <- corpus(out$merged, docnames = out$`_id`, docvars = out[,-seq(1,(length(names(out))-3),1)]) %>%
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dfm <- corpus(out$merged, docnames = out$`_id`, docvars = out[,-seq(1,(length(names(out))-3),1)]) %>%
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dfm(tolower = T, stem = F, remove_punct = T, valuetype = "regex", ngrams = 1)
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dfm(tolower = T, stem = F, remove_punct = T, valuetype = "regex", ngrams = 1)
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return(dfm)
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return(dfm)
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}
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}
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