tidy_net_sentiment_nrc: Order sentiment occurrence table by sentiment counts

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

Description

Order result of calc_net_sentiment_nrc by the count of one or more NRC sentiments, and convert to "tidy" format.

Usage

tidy_net_sentiment_nrc(
  net_sentiment_wide_nrc,
  sorting_sentiments = "anger",
  num_of_docs = 60
)

Arguments

net_sentiment_wide_nrc	A data frame/tibble derived from calc_net_sentiment_nrc.
sorting_sentiments	A string of vector of strings with the NRC sentiments (Mohammad & Turney, 2013) by which to sort the returned table. (To see all NRC sentiments run prep_sentiments_nrc). Order matters: c("anger", "fear") will sort by "anger" first. Conversely, c("fear", "anger") will sort by "fear" first.
num_of_docs	The number of document IDs for which to return results. See details.

Details

The returned data frame will have, for each document (or class- see filter_class in calc_net_sentiment_nrc), as many rows as the NRC sentiments with non-zero counts (remember- this function returns the result in long format). If all sentiments have non-zero counts in a given document, then there will be 10 rows (as many as all the NRC sentiments) in the returned data frame for this document. Argument num_of_docs returns the first num_of_docs documents (or classes, see filter_class in calc_net_sentiment_nrc), with the top one being the one that has the highest count(s) of the NRC sentiment(s) specified in sorting_sentiments.

Value

A data frame in long format with four columns: the text; the line number; the NRC sentiment; and the count of the NRC sentiment.

References

Mohammad S.M. & Turney P.D. (2013). Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29(3):436-465.

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(pp, emma),
  book = c("Pride & Prejudice", "Emma")
)

# Net sentiment in each book
net_sentiment_wide_nrc <- calc_net_sentiment_nrc(x, target_col_name = "book",
                                                 text_col_name = "text",
                                                 filter_class = NULL)

net_sentiment_wide_nrc

# Tidy net_sentiment_wide_nrc and place the most "angry" book at the top
tidy_net_sentiment_nrc(net_sentiment_wide_nrc,
                       sorting_sentiments = "anger",
                       num_of_docs = 2) %>%
  dplyr::select(-text)

# Can sort by multiple sentiments too: as above, but for most "angry" and then for most "negative"
tidy_net_sentiment_nrc(net_sentiment_wide_nrc,
                       sorting_sentiments = c("anger", "negative"),
                       num_of_docs = 2) %>%
  dplyr::select(-text)

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