Plot sentiment counts in a text

Bar plots of NRC sentiment counts in one or more documents.

plot_net_sentiment_long_nrc(net_sentiment_long_nrc)

Arguments

net_sentiment_long_nrc	A data frame from tidy_net_sentiment_nrc.

Value

A ggplot (ggplot::geom_col).

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
net_sentiment_wide_nrc <- calc_net_sentiment_nrc(x, target_col_name = "book",
                                                 text_col_name = "text",
                                                 filter_class = NULL)

net_sentiment_wide_nrc
#> # A tibble: 2 x 13
#>   text       linenumber anger anticipation disgust  fear   joy negative positive
#>   <chr>           <int> <int>        <int>   <int> <int> <int>    <int>    <int>
#> 1 "c(\"EMMA~          1  1548         4821    1323  2343  4432     4473     9471
#> 2 "c(\"PRID~          2  1295         3596     973  1767  3336     3641     7443
#> # ... with 4 more variables: sadness <int>, surprise <int>, trust <int>,
#> #   book <chr>

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

net_sentiment_long_nrc
#> # A tibble: 20 x 4
#>    linenumber book              name         value
#>    <fct>      <chr>             <fct>        <int>
#>  1 1          Emma              anger         1548
#>  2 1          Emma              anticipation  4821
#>  3 1          Emma              disgust       1323
#>  4 1          Emma              fear          2343
#>  5 1          Emma              joy           4432
#>  6 1          Emma              negative      4473
#>  7 1          Emma              positive      9471
#>  8 1          Emma              sadness       2253
#>  9 1          Emma              surprise      2220
#> 10 1          Emma              trust         5613
#> 11 2          Pride & Prejudice anger         1295
#> 12 2          Pride & Prejudice anticipation  3596
#> 13 2          Pride & Prejudice disgust        973
#> 14 2          Pride & Prejudice fear          1767
#> 15 2          Pride & Prejudice joy           3336
#> 16 2          Pride & Prejudice negative      3641
#> 17 2          Pride & Prejudice positive      7443
#> 18 2          Pride & Prejudice sadness       1755
#> 19 2          Pride & Prejudice surprise      1665
#> 20 2          Pride & Prejudice trust         4180

plot_net_sentiment_long_nrc(net_sentiment_long_nrc)