Movie review to test accuracy.R

#import libraries to work with
library(plyr)
library(stringr)
library(e1071)    
 
#load up word polarity list and format it
afinn_list <- read.delim(file='G:/Mita/Twitter Sentiment Analysis/AFINN/AFINN-111.txt', header=FALSE, stringsAsFactors=FALSE)
names(afinn_list) <- c('word', 'score')
afinn_list$word <- tolower(afinn_list$word)    
 
#categorize words as very negative to very positive and add some movie-specific words
vNegTerms <- afinn_list$word[afinn_list$score==-5 | afinn_list$score==-4]
negTerms <- c(afinn_list$word[afinn_list$score==-3 | afinn_list$score==-2 | afinn_list$score==-1], "second-rate", "moronic", "third-rate", "flawed", "juvenile", "boring", "distasteful", "ordinary", "disgusting", "senseless", "static", "brutal", "confused", "disappointing", "bloody", "silly", "tired", "predictable", "stupid", "uninteresting", "trite", "uneven", "outdated", "dreadful", "bland")
posTerms <- c(afinn_list$word[afinn_list$score==3 | afinn_list$score==2 | afinn_list$score==1], "first-rate", "insightful", "clever", "charming", "comical", "charismatic", "enjoyable", "absorbing", "sensitive", "intriguing", "powerful", "pleasant", "surprising", "thought-provoking", "imaginative", "unpretentious")
vPosTerms <- c(afinn_list$word[afinn_list$score==5 | afinn_list$score==4], "uproarious", "riveting", "fascinating", "dazzling", "legendary")    
 
#load up positive and negative sentences and format
posText <- read.delim(file='G:/Mita/Twitter Sentiment Analysis/rt-polaritydata/rt-polarity_pos.txt', header=FALSE, stringsAsFactors=FALSE)
posText <- posText$V1
posText <- unlist(lapply(posText, function(x) { str_split(x, "\n") }))
negText <- read.delim(file='G:/Mita/Twitter Sentiment Analysis/polarityData/rt-polaritydata/rt-polarity_neg.txt', header=FALSE, stringsAsFactors=FALSE)
negText <- negText$V1
negText <- unlist(lapply(negText, function(x) { str_split(x, "\n") }))    
 
#function to calculate number of words in each category within a sentence
sentimentScore <- function(sentences, vNegTerms, negTerms, posTerms, vPosTerms){
  final_scores <- matrix('', 0, 5)
  scores <- laply(sentences, function(sentence, vNegTerms, negTerms, posTerms, vPosTerms){
    initial_sentence <- sentence
    #remove unnecessary characters and split up by word 
    sentence <- gsub('[[:punct:]]', '', sentence)
    sentence <- gsub('[[:cntrl:]]', '', sentence)
    sentence <- gsub('\\d+', '', sentence)
    sentence <- tolower(sentence)
    wordList <- str_split(sentence, '\\s+')
    words <- unlist(wordList)
    #build vector with matches between sentence and each category
    vPosMatches <- match(words, vPosTerms)
    posMatches <- match(words, posTerms)
    vNegMatches <- match(words, vNegTerms)
    negMatches <- match(words, negTerms)
    #sum up number of words in each category
    vPosMatches <- sum(!is.na(vPosMatches))
    posMatches <- sum(!is.na(posMatches))
    vNegMatches <- sum(!is.na(vNegMatches))
    negMatches <- sum(!is.na(negMatches))
    score <- c(vNegMatches, negMatches, posMatches, vPosMatches)
    #add row to scores table
    newrow <- c(initial_sentence, score)
    final_scores <- rbind(final_scores, newrow)
    return(final_scores)
  }, vNegTerms, negTerms, posTerms, vPosTerms)
  return(scores)
}    
 
#build tables of positive and negative sentences with scores
posResult <- as.data.frame(sentimentScore(posText, vNegTerms, negTerms, posTerms, vPosTerms))
negResult <- as.data.frame(sentimentScore(negText, vNegTerms, negTerms, posTerms, vPosTerms))
posResult <- cbind(posResult, 'positive')
colnames(posResult) <- c('sentence', 'vNeg', 'neg', 'pos', 'vPos', 'sentiment')
negResult <- cbind(negResult, 'negative')
colnames(negResult) <- c('sentence', 'vNeg', 'neg', 'pos', 'vPos', 'sentiment')    
 
#combine the positive and negative tables
results <- rbind(posResult, negResult)    
 
#run the naive bayes algorithm using all four categories
classifier <- naiveBayes(results[,2:5], results[,6])    
 
#display the confusion table for the classification ran on the same data
confTable <- table(predict(classifier, results), results[,6], dnn=list('predicted','actual'))
confTable    
 
#run a binomial test for confidence interval of results
binom.test(confTable[1,1] + confTable[2,2], nrow(results), p=0.5)