syuzhet_mwe.r

### set the working directory---you'll need to redefine this for your local system
setwd("~/Documents/School/Kansas/Coding/2015_02_12_RCorpusLingTools/JockersDataFiles/code")

#library("syuzhet")
library("syuzhet", lib.loc="/Library/Frameworks/R.framework/Versions/3.1/Resources/library")

### define our text file (here, it's in the current working directory)
### downloaded from http://www.gutenberg.org/cache/epub/2600/pg2600.txt
text.v <- scan("WarAndPeaceEnglish.txt", what="character",sep="\n")

### this takes a while :(
s_v <- get_sentences(text.v)

### create all three vectors, one for each method
bing_vector <- get_sentiment(s_v, method="bing")
afinn_vector <- get_sentiment(s_v, method="afinn")
nrc_vector <- get_sentiment(s_v, method="nrc")

### define the method that we'll use to graph
sentiment_vector <- bing_vector
# sentiment_vector <- afinn_vector 
# sentiment_vector <- nrc_vector

### display basic summary stats if you want them
#sum(sentiment_vector); mean(sentiment_vector)
#sum(afinn_vector); mean(afinn_vector)
#sum(nrc_vector); mean(nrc_vector)

####### PLOTTING
### basic plot of sentiment totals for all sentences individually
plot(
  sentiment_vector,
  type="h",
  #main="Plot Trajectory of 'War and Peace'",
  xlab="Narrative Time",
  ylab="Emotional Valence"
)

### plot summary using percentage-based means (100 equal chunks of text)
percent_vals <- get_percentage_values(sentiment_vector)
plot(
  percent_vals,
  type="l",
  #main="Plot Trajectory of 'War and Peace'",
  xlab="Narrative Time",
  ylab="Emotional Valence",
  col="red"
)

# plot summary using Fourier Transformation and low-pass filter
ft_values <- get_transformed_values(
  sentiment_vector,
  low_pass_size=3,
  x_reverse_len=100,
  scale_vals=TRUE,
  scale_range=FALSE
)
plot(
  ft_values,
  type="h",
  #main="'War and Peace' using Transformed Values",
  xlab="Narrative Time",
  ylab="Emotional Valence",
  col="red"
)