Sentiment Analysis



The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral

Use case:

Customer’s on line comments/feedback from an insurance companies website has been scrapped to run through the sentiment analysis.

You can find full R code along with the data set in my git repository here


  • Load required R libraries

    # source("")
    # biocLite("Rgraphviz")
    # install.packages('tm')
    # install.packages('wordcloud')
    # download.file("", "Rstem_0.4-1.tar.gz")
    # install.packages("Rstem_0.4-1.tar.gz", repos=NULL, type="source")
    # download.file("", "sentiment.tar.gz")
    # install.packages("sentiment.tar.gz", repos=NULL, type="source")# Load libraries
  • Pre-process data:

    Text pre-processing is an important step to reduce noise from the data. Each step is discussed below

  • convert to lower: this is to avoid distinguish between words simply on case
  • remove punctuation: punctuation can provide grammatical context which supports understanding. Often for initial analyses we ignore the punctuation
  • remove numbers: numbers may or may not be relevant to our analyses
  • remove stop words: stop words are common words found in a language. Words like for, of, are etc are common stop word
  • create document term matrix: a document term matrix is simply a matrix with documents as the rows and terms as the columns and a count of the frequency of words as the cells of the matrix
df <- read.table("../input/data.csv",sep=",",header=TRUE)
corp <- Corpus(VectorSource(df$Review)) 
corp <- tm_map(corp, tolower) 
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, removeNumbers)
# corp <- tm_map(corp, stemDocument, language = "english") 
corp <- tm_map(corp, removeWords, c("the", stopwords("english"))) 
corp <- tm_map(corp, PlainTextDocument)
corp.tdm <- TermDocumentMatrix(corp, control = list(minWordLength = 3)) 
corp.dtm <- DocumentTermMatrix(corp, control = list(minWordLength = 3))
  • Insight through visualization

    • Word cloud: This visualization generates words whose font size relates to its frequency.

      wordcloud(corp, scale=c(5,0.5), max.words=100, random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8, 'Dark2'))

      Word Cloud

    • Frequency plot:This visualization presents a bar chart whose length corresponds the frequency a particular word occurred
      corp.tdm.df <- sort(rowSums(corp.tdm.df),decreasing=TRUE) # populate term frequency and sort in decesending order
      df.freq <- data.frame(word = names(corp.tdm.df),freq=corp.tdm.df) # Table with terms and frequency
      # Set minimum term frequency value. The charts will be created for terms > or = to the minimum value that we set.
      freqControl <- 100
      # Frequency Plot
      freqplotData <- subset(df.freq, df.freq$freq > freqControl)
      freqplotData$word <- ordered(freqplotData$word,levels=levels(freqplotData$word)[unclass(freqplotData$word)])
      freqplot <- ggplot(freqplotData,aes(reorder(word,freq), freq))
      freqplot <- freqplot + geom_bar(stat="identity")
      freqplot <- freqplot + theme(axis.text.x=element_text(angle=90,hjust=1)) + coord_flip() 
      freqplot + xlim(rev(levels(freqplotData$word)))+ ggtitle("Frequency Plot")


    • Correlation plot: Here, we choose N number of high frequent words as the nodes and include links between words when they have at least a correlation of x %
      # Correlation Plot
      # 50 of the more frequent words have been chosen as the nodes and include links between words
      # when they have at least a correlation of 0.2
      # By default (without providing terms and a correlation threshold) the plot function chooses a
      # random 20 terms with a threshold of 0.7
      plot(corp.tdm,terms=findFreqTerms(corp.tdm,lowfreq=freqControl)[1:50],corThreshold=0.2, main="Correlation Plot")


    • Paired word cloud: This is a customized word cloud. Here, we pick the top N most frequent words and extract associated words with strong correlation. Combine individual top N words with the every associated word (say one of my top words is broken and one of the associated words is pipe; the combined word would be broken-pipe). Then we create a word cloud on the combined words. Although the concept is good, the chart below does not appear helpful. So need to figure out a better representation

      # Paired-Terms wordcloud
      # pick the top N most frequent words and extract associated words with strong correlation (say 70%). 
      # Combine individual top N words with every associated word.
      nFreqTerms <- findFreqTerms(corp.dtm,lowfreq=freqControl)
      nFreqTermsAssocs <- findAssocs(corp.dtm, nFreqTerms, 0.3)
      pairedTerms <- c()
      for (i in 1:length(nFreqTermsAssocs)){
          lapply(names(nFreqTermsAssocs[[i]]),function(x) pairedTerms <<- c(pairedTerms,paste(names(nFreqTermsAssocs[i]),x,sep="-")))
      wordcloud(pairedTerms,random.order=FALSE,colors=dark2,main="Paired Wordcloud")

      Paired Word Cloud

  • Sentiment Score

    • Load positive / negative terms corpus

      The corpus contains around 6800 words, this list was compiled over many years starting from first paper by Hu and Liu, KDD-2004. Although necessary, having an opinion lexicon is far from sufficient for accurate sentiment analysis. See this paper: Sentiment Analysis and Subjectivity or the Sentiment Analysis

    • Calculate positive / negative score

      Simply we calculate the positive / negative score by comparing the terms with positive/negative term corpus and summing the occurrence count

    • Classify emotion

      R package sentiment by Timothy Jurka has a function that 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.

    • Classify polarity

      Another function from sentiment package, classify_polarity 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.

      hu.liu.pos = scan('../input/positive-words.txt', what = 'character',comment.char=';') 
      hu.liu.neg = scan('../input/negative-words.txt',what = 'character',comment.char= ';') 
      pos.words = c(hu.liu.pos)
      neg.words = c(hu.liu.neg)
      score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
        # we got a vector of sentences. plyr will handle a list
        # or a vector as an "l" for us
        # we want a simple array ("a") of scores back, so we use
        # "l" + "a" + "ply" = "laply":
        scores = laply(sentences, function(sentence, pos.words, neg.words) {
          # clean up sentences with R's regex-driven global substitute, gsub():
          sentence = gsub('[[:punct:]]', '', sentence)
          sentence = gsub('[[:cntrl:]]', '', sentence)
          sentence = gsub('\\d+', '', sentence)
          # and convert to lower case:
          sentence = tolower(sentence)
          # split into words. str_split is in the stringr package
          word.list = str_split(sentence, '\\s+')
          # sometimes a list() is one level of hierarchy too much
          words = unlist(word.list)
          # compare our words to the dictionaries of positive & negative terms
          pos.matches = match(words, pos.words)
          neg.matches = match(words, neg.words)
          # match() returns the position of the matched term or NA
          # we just want a TRUE/FALSE:
          pos.matches= !
          neg.matches= !
          # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
          score = sum(pos.matches) - sum(neg.matches)
        }, pos.words, neg.words, .progress=.progress )
        scores.df = data.frame(score=scores, text=sentences)
      review.scores<- score.sentiment(df$Review,pos.words,neg.words,.progress='text')
      #classify emotion
      class_emo = classify_emotion(df$Review, 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(df$Review, algorithm="bayes")
      # get polarity best fit
      polarity = class_pol[,4]
      # data frame with results
      sent_df = data.frame(text=df$Review, emotion=emotion, polarity=polarity, stringsAsFactors=FALSE)
      # sort data frame
      sent_df = within(sent_df, emotion <- factor(emotion, levels=names(sort(table(emotion), decreasing=TRUE))))
    • Visualize

      • Distribution of overall score
        ggplot(review.scores, aes(x=score)) + 
          geom_histogram(binwidth=1) + 
          xlab("Sentiment score") + 
          ylab("Frequency") + 
          ggtitle("Distribution of sentiment score") +
          theme_bw()  + 
          theme(axis.title.x = element_text(vjust = -0.5, size = 14)) + 
          theme(axis.title.y=element_text(size = 14, angle=90, vjust = -0.25)) + 
          theme(plot.margin = unit(c(1,1,2,2), "lines"))

        Sentiment Score Distribution.png

      • Distribution of score for a given term
        review.pos<- subset(review.scores,review.scores$score>= 2) 
        review.neg<- subset(review.scores,review.scores$score<= -2)
        claim <- subset(review.scores, regexpr("claim", review.scores$text) > 0) 
        ggplot(claim, aes(x = score)) + geom_histogram(binwidth = 1) + ggtitle("Sentiment score for the token 'claim'") + xlab("Score") + ylab("Frequency") + theme_bw()  + theme(axis.title.x = element_text(vjust = -0.5, size = 14)) + theme(axis.title.y = element_text(size = 14, angle = 90, vjust = -0.25)) + theme(plot.margin = unit(c(1,1,2,2), "lines"))

        Claim Score.png

      • Distribution of emotion
        # 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 Feedback", 
               title = "Sentiment Analysis of Feedback about claim(classification by emotion)",
               plot.title = element_text(size=12))

        Emotions Distribution.png

      • Distribution of polarity
        # plot distribution of polarity
        ggplot(sent_df, aes(x=polarity)) +
          geom_bar(aes(y=..count.., fill=polarity)) +
          scale_fill_brewer(palette="RdGy") +
          labs(x="emotion categories", y="number of Feedback", 
               title = "Sentiment Analysis of Feedback about claim(classification by emotion)",
               plot.title = element_text(size=12))


      • Text by emotion
        # separating text by emotion
        emos = levels(factor(sent_df$emotion))
        nemo = length(emos) = rep("", nemo)
        for (i in 1:nemo)
          tmp = df$Review[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)

        Text by emotion.png

Next post to cover sentiment analysis in R + Hadoop.



The above write up is based on the tutorials from following links: