sentiment.R

#' 
#' score.sentiment() implements a very simple algorithm to estimate
#' sentiment, assigning a integer score by subtracting the number 
#' of occurrences of negative words from that of positive words.
#' 
#' @param sentences vector of text to score
#' @param pos.words vector of words of postive sentiment
#' @param neg.words vector of words of negative sentiment
#' @param .progress passed to <code>laply()</code> to control of progress bar.
#' @returnType data.frame
#' @return data.frame of text and corresponding sentiment scores
#' @author Jefrey Breen <jbreen@cambridge.aero>
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
	require(plyr)
	require(stringr)
	
	# we got a vector of sentences. plyr will handle a list or a vector as an "l" for us
	# we want a simple array 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 = !is.na(pos.matches)
		neg.matches = !is.na(neg.matches)

		# and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
		score = sum(pos.matches) - sum(neg.matches)

		return(score)
	}, pos.words, neg.words, .progress=.progress )

	scores.df = data.frame(score=scores, text=sentences)
	return(scores.df)
}