sentimentr (version 2.7.1)

extract_sentiment_terms: Extract Sentiment Words

Description

Extract the sentiment words from a text.

Usage

extract_sentiment_terms(text.var,
  polarity_dt = lexicon::hash_sentiment_jockers_rinker, hyphen = "",
  ...)

Arguments

text.var

The text variable.

polarity_dt

A data.table of positive/negative words and weights with x and y as column names.

hyphen

The character string to replace hyphens with. Default replaces with nothing so 'sugar-free' becomes 'sugarfree'. Setting hyphen = " " would result in a space between words (e.g., 'sugar free').

…

Ignored.

Value

Returns a data.table with columns of positive and negative terms. In addition, the attributes $counts and $elements return an aggregated count of the usage of the words and a detailed sentiment score of each word use. See the examples for more.

Examples

# NOT RUN {
library(data.table)
set.seed(10)
x <- get_sentences(sample(hu_liu_cannon_reviews[[2]], 1000, TRUE))
sentiment(x)

pol_words <- extract_sentiment_terms(x)
pol_words
pol_words$sentence
pol_words$neutral
data.table::as.data.table(pol_words)

attributes(extract_sentiment_terms(x))$counts
attributes(extract_sentiment_terms(x))$elements

# }
# NOT RUN {
library(wordcloud)
library(data.table)

set.seed(10)
x <- get_sentences(sample(hu_liu_cannon_reviews[[2]], 1000, TRUE))
sentiment_words <- extract_sentiment_terms(x)

sentiment_counts <- attributes(sentiment_words)$counts
sentiment_counts[polarity > 0,]

par(mfrow = c(1, 3), mar = c(0, 0, 0, 0))
## Positive Words
with(
    sentiment_counts[polarity > 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"), scale = c(4.5, .75)
    )
)
mtext("Positive Words", side = 3, padj = 5)

## Negative Words
with(
    sentiment_counts[polarity < 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = brewer.pal(8, "Dark2"), scale = c(4.5, 1)
    )
)
mtext("Negative Words", side = 3, padj = 5)

sentiment_counts[, 
    color := ifelse(polarity > 0, 'red', 
        ifelse(polarity < 0, 'blue', 'gray70')
    )]

## Positive & Negative Together
with(
    sentiment_counts[polarity != 0,],
    wordcloud(words = words, freq = n, min.freq = 1,
          max.words = 200, random.order = FALSE, rot.per = 0.35,
          colors = color, ordered.colors = TRUE, scale = c(5, .75)
    )
)
mtext("Positive (red) & Negative (blue) Words", side = 3, padj = 5)
# }