syuzhet

syuzhet

Sentiment extraction and analysis.

Basic Sentiment

library(syuzhet)

# Get sentiment scores
text <- "I love this amazing product! It's wonderful."
get_sentiment(text)

# Multiple sentences
sentences <- c("I love this!", "This is terrible.", "It's okay.")
get_sentiment(sentences)

Sentiment Methods

# Syuzhet (default)
get_sentiment(text, method = "syuzhet")

# Bing
get_sentiment(text, method = "bing")

# AFINN
get_sentiment(text, method = "afinn")

# NRC
get_sentiment(text, method = "nrc")

# Stanford (requires Java)
get_sentiment(text, method = "stanford")

NRC Emotions

# Get emotion scores
emotions <- get_nrc_sentiment(text)

# Returns data frame with columns:
# anger, anticipation, disgust, fear, joy,
# sadness, surprise, trust, negative, positive

# Multiple texts
texts <- c("I'm so happy!", "This makes me angry.", "I'm scared.")
emotions <- get_nrc_sentiment(texts)

Sentiment by Sentence

# Split into sentences
sentences <- get_sentences(text)

# Get sentiment for each
sentiment <- get_sentiment(sentences)

# Plot sentiment arc
plot(sentiment, type = "l")

Sentiment Transformation

# Get sentiment values
sentiment <- get_sentiment(sentences)

# Smooth with DCT
dct_values <- get_dct_transform(sentiment, low_pass_size = 5)

# Percentage-based transformation
pct_values <- get_percentage_values(sentiment, bins = 10)

# Plot transformed sentiment
simple_plot(dct_values)

Sentiment Arcs

# Get sentiment arc
sentiment <- get_sentiment(sentences)

# Rescale to 0-1
rescaled <- rescale_x_2(sentiment)

# Plot arc
plot(rescaled, type = "l", main = "Sentiment Arc")

Word Tokens

# Get tokens
tokens <- get_tokens(text)

# Get sentiment for tokens
token_sentiment <- get_sentiment(tokens)

Custom Lexicon

# Load custom lexicon
custom_lexicon <- data.frame(
  word = c("awesome", "terrible", "meh"),
  value = c(2, -2, 0)
)

# Use custom method
get_sentiment(text, method = "custom", lexicon = custom_lexicon)