Score.SentimentFunction.R

#Jeffrey Breen's approach
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
  #Parameters
  #sentences: a vector of text
  #pos.words: a vector of words of postive sentiment
  #neg.words: a vector of words of negative sentiment
  #.progress: passed to laply() to control of progress bar: It shows the time elapsed in the console/terminal
  
  #Creating an array of scores using laply
  scores = laply(sentences, function(sentence, pos.words, neg.words)
                 {
                   #Removing punctuation using global substitute
                   sentence = gsub("[[:punct:]]", "", sentence)
                   #Removing control characters
                   sentence = gsub("[[:cntrl:]]", "", sentence)
                   #Removing digits
                   sentence = gsub('\\d+', '', sentence)
                   
                   #Error handling while trying to get the text in lower case
                   tryTolower = function(x)
                   {
                     #Create missing value
                     y = NA
                     #Try Catch error
                     try_error = tryCatch(tolower(x), error=function(e) e)
                     #If not an error
                     if (!inherits(try_error, "error"))
                       y = tolower(x)
                     #Return the result
                     return(y)
                   }
                                          
                   #Use this tryTolower function in sapply
                   sentence = sapply(sentence, tryTolower)
                   
                   #Split sentences into words with str_split (stringr package)
                   word.list = str_split(sentence, "\\s+")
                   #Unlist produces a vector which contains all atomic components in word.list
                   words = unlist(word.list)
                   
                   #Compare these words to the dictionaries of positive & negative words
                   pos.matches = match(words, pos.words)
                   neg.matches = match(words, neg.words)
                   #Example: If a sentence is "He is a good boy", 
                   #then, pos.matches returns: [NA, NA, NA, *some number*, NA] : the number depends on the severity of the word is my guess
                   #neg.matches returns: [NA, NA, NA, NA, NA] 
                   #So the output has NAs and numbers
                 
                   #We just want a TRUE/FALSE value for the pos.matches & neg.matches
                   #Getting the position of the matched term or NA
                   pos.matches = !is.na(pos.matches)
                   neg.matches = !is.na(neg.matches)
                   #This would return : [F, F, F, T, F] depending on the NA or the match
                                          
                   #The TRUE or FALSE values are treated as 1/0
                   #To get the final score of the sentence:
                   score = sum(pos.matches) - sum(neg.matches)
                   return(score)
                 }, pos.words, neg.words, .progress=.progress )
  
  #Now the scores are put in a dataframe and returned
  scores.df = data.frame(text=sentences, score=scores)
  return(scores.df)
}