calc_bigrams_network: Create and count bigrams

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

Description

For a given labelled text, create and calculate the most frequently occurring bigrams (no stop words) for the given class(es).

Usage

calc_bigrams_network(
  x,
  target_col_name,
  text_col_name,
  filter_class = NULL,
  bigrams_prop
)

Arguments

x	A data frame with one or more columns: the column with the classes (if target_col_name is not NULL); and the column with the text. Any other columns will be ignored.
target_col_name	A string with the column name of the target variable. Defaults to NULL.
text_col_name	A string with the column name of the text variable.
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).
bigrams_prop	A numeric in (0, 100] indicating the percentage of the most frequent bigrams to keep.

Value

A data frame with three columns: first word of bigram; second word of bigram; and bigram count.

Note

When supplying more than one class in filter_class, the returned data frame will NOT separate the results for the different classes. If separation is desired, then run the function for each class separately or do something like this:

# Assuming that the class and text columns are called "label" and
# "feedback" respectively
x %>%
    split(.$label) %>%
    purrr::map(
        ~ calc_bigrams_network(., target_col_name = NULL,
                               text_col_name = "feedback",
                               filter_class = NULL, bigrams_prop = 50)
    )

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")
)

# Bigrams for both books
calc_bigrams_network(x, target_col_name = "book", text_col_name = "text",
                     filter_class = NULL, bigrams_prop = 3)

# Bigrams for Emma
calc_bigrams_network(x, target_col_name = "book", text_col_name = "text",
                     filter_class = "Emma", bigrams_prop = 3)

# Bigrams for Pride & Prejudice
calc_bigrams_network(x, target_col_name = "book", text_col_name = "text",
                     filter_class = "Pride & Prejudice", bigrams_prop = 3)

CDU-data-science-team/experienceAnalysis documentation built on Dec. 17, 2021, 12:53 p.m.