## Case Study: IMDb Movie Reviews
## Authors:
## - "Stefan Feuerriegel"
## - "Nicolas Proellochs"
library(SentimentAnalysis)
if (file.exists("imdb.Rdata")) {
load("imdb.Rdata")
} else {
# Load IMDb dataset from the Internet (this might take a few seconds)
imdb <- loadImdb()
# Save locally for further usages
save(imdb, file="imdb.Rdata")
}
# Analyze sentiment
sentiment <- analyzeSentiment(imdb$Corpus)
# Statistical and visual evaluation
compareToResponse(sentiment, imdb$Rating)
plotSentimentResponse(sentiment$SentimentGI, imdb$Rating)
# Generate Dictionary
dict_imdb <- generateDictionary(imdb$Corpus, imdb$Rating)
summary(dict_imdb)
# Compare entries to built-in, static dictionary
compareDictionaries(dict_imdb,
loadDictionaryGI())
# Show estimated coefficients with Kernel Density Estimation (KDE)
plot(dict_imdb)
plot(dict_imdb) + xlim(c(-0.1, 0.1)) # with nicer axis
# Compute in-sample performance
pred_sentiment <- predict(dict_imdb, imdb$Corpus)
plotSentimentResponse(pred_sentiment, imdb$Rating)
perf_dictionary <- compareToResponse(pred_sentiment, imdb$Rating)
perf_sentimentGI <- compareToResponse(sentiment$SentimentGI, imdb$Rating)
# Comparison
perf_dictionary
perf_sentimentGI
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