Twitter Sentiment Analysis in R

Twitter Sentiment Analysis in R I have a JSON file which contains a sample of tweets. I would like to analyse these for sentiment in R. I have one method of analysing sentiment, shown below, however I am open to ideas on how to improve the method. Attached are 3 files: JSON [login to view URL] – the JSON file containing the tweets [login to view URL] – a lexicon of positive words [login to view URL] – a lexicon of negative words Please provide an R script which: 1. Imports the tweets in the JSON file into R as a dataframe 2. Identifies the tweets themselves (the $body field in the JSON file) 3. Allows sentiment analysis in the form demonstrated below, or an improved form of your choosing 4. Is useable in real time with a live JSON stream as well as the static file attached Additionally, please provide workspace images showing the result of the script. Thank you very much in advance! Sentiment analysis method currently used: library (plyr) library(stringr) # function to score sentiment [login to view URL] = function(sentences, [login to view URL], [login to view URL], .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, [login to view URL], [login to view URL]) { # 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 [login to view URL] = str_split(sentence, '\\s+') # sometimes a list() is one level of hierarchy too much words = unlist([login to view URL]) # compare our words to the dictionaries of positive & negative terms [login to view URL] = match(words, [login to view URL]) [login to view URL] = match(words, [login to view URL])pos # match() returns the position of the matched term or NA # we just want a TRUE/FALSE: [login to view URL] =  [login to view URL] =  # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum(): score = sum([login to view URL]) - sum([login to view URL]) return(score) }, [login to view URL], [login to view URL], .progress=.progress ) [login to view URL] = [login to view URL](score=scores, text=sentences) return([login to view URL]) } #load positive and negative word lexicons pos.words.file=scan('[login to view URL]',what='character',comment.char=';') neg.words.file=scan('[login to view URL]',what='character',comment.char=';') pos.words=c([login to view URL]) neg.words=c([login to view URL]) #run function on dataframe named 'tweets' sentiment=[login to view URL](tweets,[login to view URL],[login to view URL],.progress='text') #total score for all tweets sentiment.total=sum(sentiment$score) [login to view URL]