R/calc_net_sentiment_nrc.R

In CDU-data-science-team/experienceAnalysis: Helper Functions for Text Mining

#' Calculate "net sentiment" in a text
#'
#' For a given text, translate all words into NRC sentiments and count sentiment
#' occurrences.
#'
#' @param x A data frame with two columns: the column with the classes; and the
#'     column with the text. Any other columns will be ignored.
#' @param target_col_name A string with the column name of the target variable.
#'     Defaults to `NULL`.
#' @param text_col_name A string with the column name of the text variable.
#' @param filter_class A string or vector of strings with the name(s) of the
#'     class(es) for which bigrams are to be created and counted. Defaults to
#'     `NULL` (all rows).
#'
#' @return A data frame with 12 or 13 columns: the text column; the line number;
#'     the 10 NRC sentiments (anger, anticipation disgust, fear, joy, negative,
#'     positive, sadness, surprise, trust- see Mohammad & Turney, 2013); and the
#'     column with the classes (if any).
#' @export
#'
#' @examples
#' library(experienceAnalysis)
#' books <- janeaustenr::austen_books() # Jane Austen books
#' emma <- paste(books[books$book == "Emma", ], collapse = " ") # String with whole book
#' pp <- paste(books[books$book == "Pride & Prejudice", ], collapse = " ") # String with whole book
#'
#' # Make data frame with books Emma and Pride & Prejudice
#' x <- data.frame(
#'   text = c(emma, pp),
#'   book = c("Emma", "Pride & Prejudice")
#' )
#'
#' # Net sentiment in each book
#' calc_net_sentiment_nrc(x, target_col_name = "book", text_col_name = "text",
#'                        filter_class = NULL)
#' @references Mohammad S.M. & Turney P.D. (2013). Crowdsourcing a
#'     Word–Emotion Association Lexicon. Computational Intelligence,
#'     29(3):436-465.

calc_net_sentiment_nrc <- function(x, target_col_name = NULL, text_col_name,
                                   filter_class = NULL) {

  # Check and load NRC sentiment dictionary (Mohammad & Turney, 2013)
  nrc <- get_dictionary("nrc")

  nrc_sentiments <- experienceAnalysis::prep_sentiments_nrc()

  text_data_filtered <- x %>%
    #dplyr::filter(super != "Couldn't be improved") %>%
    dplyr::mutate(linenumber = dplyr::row_number()) %>%
    dplyr::filter(
      dplyr::across(
        dplyr::all_of(target_col_name),
        ~ experienceAnalysis::tidy_filter_null(., filter_class,
                                               filter_how = "in")
      )
    )

  net_sentiment_nrc <- text_data_filtered %>%
    tidytext::unnest_tokens(word, !! rlang::sym(text_col_name)) %>%
    dplyr::left_join(nrc, by = "word") %>% # We want a left join so as not to lose comments with no sentiment
    dplyr::count(linenumber, sentiment, name = "sentiment_count") %>%
    dplyr::mutate(
      sentiment_count =
        dplyr::case_when(
          is.na(sentiment) ~ NA_integer_,
          TRUE ~ sentiment_count
        )
    ) %>%
    dplyr::select(linenumber, sentiment, sentiment_count) %>%
    tidyr::pivot_wider(names_from = sentiment,
                       values_from = sentiment_count,
                       values_fill = 0,
                       names_sort = TRUE
    ) %>%
    dplyr::left_join(text_data_filtered, by = "linenumber") %>%
    dplyr::select(
      dplyr::all_of(text_col_name),
      dplyr::everything(),
      -`NA`
    )

    return(net_sentiment_nrc)
}