Sentiment Analysis with "sentiment"    sentiments

R package "sentiment"
Another interesting option that we can use to do our sentiment analysis is by utilizing the R package sentiment by Timothy Jurka. This package contains two handy functions serving our purposes:

This function helps us to analyze some text and classify it in different types of emotion: anger, disgust, fear, joy, sadness, and surprise. The classification can be performed using two algorithms: one is a naive Bayes classifier trained on Carlo Strapparava and Alessandro Valitutti’s emotions lexicon; the other one is just a simple voter procedure.

In contrast to the classification of emotions, the classify_polarity function allows us to classify some text as positive or negative. In this case, the classification can be done by using a naive Bayes algorithm trained on Janyce Wiebe’s subjectivity lexicon; or by a simple voter algorithm.

Important Note:
The R package "sentiment" depends on Duncan's Temple Rstem package that is only available at Omegahat
At the time of this writing, I'm using the version 0.4-1 (I downloaded and installed the tar.gz file from the package website).

Example with tweets talking about "starbucks"

Step 1: Load the necessary packages
# required pakacges

Step 2: Let's collect some tweets containing the term "starbucks" 
# harvest some tweets
some_tweets = searchTwitter("starbucks", n=1500, lang="en")

# get the text
some_txt = sapply(some_tweets, function(x) x$getText())

Step 3: Prepare the text for sentiment analysis
# remove retweet entities
some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
# remove at people
some_txt = gsub("@\\w+", "", some_txt)
# remove punctuation
some_txt = gsub("[[:punct:]]", "", some_txt)
# remove numbers
some_txt = gsub("[[:digit:]]", "", some_txt)
# remove html links
some_txt = gsub("http\\w+", "", some_txt)
# remove unnecessary spaces
some_txt = gsub("[ \t]{2,}", "", some_txt)
some_txt = gsub("^\\s+|\\s+$", "", some_txt)

# define "tolower error handling" function 
try.error = function(x)
   # create missing value
   y = NA
   # tryCatch error
   try_error = tryCatch(tolower(x), error=function(e) e)
   # if not an error
   if (!inherits(try_error, "error"))
   y = tolower(x)
   # result
# lower case using try.error with sapply 
some_txt = sapply(some_txt, try.error)

# remove NAs in some_txt
some_txt = some_txt[!]
names(some_txt) = NULL

Step 4: Perform Sentiment Analysis
# classify emotion
class_emo = classify_emotion(some_txt, algorithm="bayes", prior=1.0)
# get emotion best fit
emotion = class_emo[,7]
# substitute NA's by "unknown"
emotion[] = "unknown"

# classify polarity
class_pol = classify_polarity(some_txt, algorithm="bayes")
# get polarity best fit
polarity = class_pol[,4]

Step 5: Create data frame with the results and obtain some general statistics
# data frame with results
sent_df = data.frame(text=some_txt, emotion=emotion,
polarity=polarity, stringsAsFactors=FALSE)

# sort data frame
sent_df = within(sent_df,
  emotion <- factor(emotion, levels=names(sort(table(emotion), decreasing=TRUE))))

This what the first 15 rows of sent_df would look like

Step 6: Let's do some plots of the obtained results
# plot distribution of emotions
ggplot(sent_df, aes(x=emotion)) +
geom_bar(aes(y=..count.., fill=emotion)) +
scale_fill_brewer(palette="Dark2") +
labs(x="emotion categories", y="number of tweets") +
opts(title = "Sentiment Analysis of Tweets about Starbucks\n(classification by emotion)",
     plot.title = theme_text(size=12))

# plot distribution of polarity
ggplot(sent_df, aes(x=polarity)) +
geom_bar(aes(y=..count.., fill=polarity)) +
scale_fill_brewer(palette="RdGy") +
labs(x="polarity categories", y="number of tweets") +
opts(title = "Sentiment Analysis of Tweets about Starbucks\n(classification by polarity)",
     plot.title = theme_text(size=12))

Step 7: Separate the text by emotions and visualize the words with a comparison cloud
# separating text by emotion
emos = levels(factor(sent_df$emotion))
nemo = length(emos) = rep("", nemo)
for (i in 1:nemo)
   tmp = some_txt[emotion == emos[i]][i] = paste(tmp, collapse=" ")

# remove stopwords = removeWords(, stopwords("english"))
# create corpus
corpus = Corpus(VectorSource(
tdm = TermDocumentMatrix(corpus)
tdm = as.matrix(tdm)
colnames(tdm) = emos

# comparison word cloud, colors = brewer.pal(nemo, "Dark2"),
   scale = c(3,.5), random.order = FALSE, title.size = 1.5)