Created
January 6, 2013 21:20
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#import libraries to work with | |
library(plyr) | |
library(stringr) | |
library(e1071) | |
#load up word polarity list and format it | |
afinn_list <- read.delim(file='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"confused", "disappointing", "bloody", "silly", "tired", "predictable", "stupid", "uninteresting", "trite", "uneven", "outdated", "dreadful", "bland") | |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='polarityData/rt-polaritydata/rt-polarity-pos.txt', header=FALSE, stringsAsFactors=FALSE) | |
posText <- posText$V1 | |
posText <- unlist(lapply(posText, function(x) { str_split(x, "\n") })) | |
negText <- read.delim(file='polarityData/rt-polaritydata/rt-polarity-neg.txt', header=FALSE, stringsAsFactors=FALSE) | |
negText <- negText$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) |
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