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reader.py
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reader.py
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__author__ = 'Purav'
import os
import nltk
from nltk.tokenize import sent_tokenize
import random
from nltk.tokenize import regexp_tokenize
from nltk.corpus import stopwords
from nltk.corpus import words
from nltk.util import ngrams
hotels = ['hotel','room','staff','hilton','james','monaco','sofitel','affinia','ambassador','hardrock','talbott','conrad','fairmont','hyatt','omni','homewood','knickerbocker','sheraton','swissotel','allegro','amalfi','intercontinental','palmer']
def writeReviews(rootdir,output):
words = 0
fo = open(output,"w")
for folder, subs, files in os.walk(rootdir):
for filename in files:
with open(os.path.join(folder,filename),'r') as src:
review = src.read()
fo.write(review+"\n\n\n")
fo.close()
def get_unigrams(review,polarity):
features = {}
features['polarity'] = polarity
review = nltk.word_tokenize(review)
unigrams = ngrams(review,1)
for unigram in unigrams:
if unigram in features.keys():
features[unigram]+=1
else:
features[unigrams]=1
return features
def get_trigrams(review,polarity):
features = {}
features['polarity'] = polarity
review = nltk.word_tokenize(review)
unigrams = ngrams(review,3)
for unigram in unigrams:
if unigram in features.keys():
features[unigram]+=1
else:
features[unigrams]=1
return features
def get_bigrams(review,polarity):
features = {}
features['polarity'] = polarity
review = nltk.word_tokenize(review)
bigrams = ngrams(review,2)
trigrams = ngrams(review,3)
unigrams = ngrams(review,1)
for unigram in unigrams:
if unigram in features.keys():
features[unigram]+=1
else:
features[unigrams]=1
for bigram in bigrams:
if bigram in features.keys():
features[bigram]+=1
else:
features[bigram]=1
for trigram in trigrams:
if trigram in features.keys():
features[trigram]+=1
else:
features[trigram]=1
return features
def getReviews(rootdir):
reviews = []
unique = []
for folder, subs, files in os.walk(rootdir):
for filename in files:
with open(os.path.join(folder,filename),'r') as src:
review = src.read()
words = regexp_tokenize(review,"\w+")
for word in words:
unique.append(word)
reviews.append(review)
return reviews
def countW(rootdir):
reviews = []
unique = []
for folder, subs, files in os.walk(rootdir):
for filename in files:
with open(os.path.join(folder,filename),'r') as src:
review = src.read()
words = regexp_tokenize(review,"\w+")
for word in words:
unique.append(word)
reviews.append(review)
unique = set(unique)
uniqueR = []
for w in unique:
if w not in stopwords.words('english'):
uniqueR.append(w)
print (len(set(uniqueR)))
def get_sentimentFeatures(review,polarity):
features = {}
features['polarity'] = polarity
words = nltk.word_tokenize(review)
x = nltk.pos_tag(words)
for word, pos in set(x):
if pos == 'JJ' or pos == 'RB' or pos == 'NNS' or pos == 'NNP':
features[word] = True
return features
def calculateAGARI(rootdir):
avgARI = 0
count = 0
uniqueWords = 0
personalRatio = 0
dollarCount = 0
personalPronouns = ["i","me","we","our","ours","mine"]
hotelName = 0
for folder, subs, files in os.walk(rootdir):
for filename in files:
with open(os.path.join(folder, filename), 'r') as src:
review = src.read()
personal = 0
sentences = sent_tokenize(review)
s = len(sentences)
capitals = 0
words = regexp_tokenize(review,"\w+")
for x in words:
if x in personalPronouns:
personal+=1
if x in hotels:
hotelName+=1
w = len(words)
unique = len(set(words))
uniqueWords+=unique
review = review.replace(" ","")
flag = "f"
for i in range(len(review)):
if review[i].isupper():
capitals+=1
if review[i] == '$':
flag = "t"
if flag=="t":
dollarCount+=1
c = len(review)
ari =4.71*(float(c)/w)+0.5*(float(w)/s)-21.43
avgARI += ari
count += 1
personalRatio += float(personal)/w
#print(nltk.ne_chunk(review))
print("\n"+rootdir)
print("ARI : "+str(float(avgARI/count)))
print("Unique words"+" "+str(uniqueWords/float(count)))
print("Ratio personal : "+str(personalRatio/float(count)))
print("DollarCount :"+str(dollarCount))
def get_features(review,polarity):
features = {}
uniqueWords = 0
personalRatio = 0
personal = 0
misspelt = 0
hotelName = 0
personalPronouns = ["i","me","we","our","ours","mine"]
sentences = sent_tokenize(review)
sent = nltk.word_tokenize(review)
s = len(sentences)
wordsR = regexp_tokenize(review,"\w+")
for x in wordsR:
if x in personalPronouns:
personal+=1
#if x not in set(words.words()):
#misspelt+=1
if x in hotels:
hotelName+=1
w = len(wordsR)
unique = len(set(wordsR))
uniqueWords+=unique
review = review.replace(" ","")
c = len(review)
cap = 0
features['dollar'] = False
for i in range(len(review)):
if review[i].isupper:
cap+=1
if review[i] == '$':
features['dollar'] = True
ari =4.71*(float(c)/w)+0.5*(float(w)/s)-21.43
capRatio = c/float(s)
personalRatio += float(personal)/w
features['uniqueWords'] = uniqueWords
features['personalRatio'] = personalRatio
features['ari'] = ari
features['capRatio'] = capRatio
features['polarity'] = polarity
features['hotel'] = hotelName
ngrams = get_bigrams(review,'x')
sentiments = get_sentimentFeatures(review,'x')
for x in ngrams.keys():
features[x] = ngrams[x]
for x in sentiments.keys():
features[x] = sentiments[x]
features['misspelt'] = misspelt
return features
'''
calculateAGARI("negative_polarity/deceptive_from_MTurk")
calculateAGARI("negative_polarity/truthful_from_Web")
calculateAGARI("positive_polarity/deceptive_from_MTurk")
calculateAGARI("positive_polarity/truthful_from_TripAdvisor")
'''
pos_deceptive = getReviews("positive_polarity/deceptive_from_MTurk")
pos_truthful = getReviews("positive_polarity/truthful_from_TripAdvisor")
neg_deceptive = getReviews("negative_polarity/deceptive_from_MTurk")
neg_truthful = getReviews("negative_polarity/truthful_from_Web")
countW("root")
featureSets = []
posFeatureSet = []
negFeatureSet = []
count = 0
nGramFeatures= []
sentimentFeatures = []
count = 1
for review in pos_deceptive:
print(count)
count+=1
nGramFeatures.append((get_bigrams(review,'positive'),'deceptive'))
featureSets.append((get_features(review,'positive'),'deceptive'))
posFeatureSet.append((get_features(review,'positive'),'deceptive'))
sentimentFeatures.append((get_sentimentFeatures(review,'positive'),'deceptive'))
for review in pos_truthful:
print(count)
count+=1
nGramFeatures.append((get_bigrams(review,'positive'),'truthful'))
featureSets.append((get_features(review,'positive'),'truthful'))
posFeatureSet.append((get_features(review,'positive'),'truthful'))
sentimentFeatures.append((get_sentimentFeatures(review,'positive'),'truthful'))
for review in neg_deceptive:
print(count)
count+=1
nGramFeatures.append((get_bigrams(review,'negative'),'deceptive'))
negFeatureSet.append((get_features(review,'negative'),'deceptive'))
featureSets.append((get_features(review,'negative'),'deceptive'))
sentimentFeatures.append((get_sentimentFeatures(review,'negative'),'deceptive'))
for review in neg_truthful:
print(count)
count+=1
nGramFeatures.append((get_bigrams(review,'negative'),'truthful'))
featureSets.append((get_features(review,'negative'),'truthful'))
negFeatureSet.append((get_features(review,'negative'),'truthful'))
sentimentFeatures.append((get_sentimentFeatures(review,'negative'),'truthful'))
random.shuffle(featureSets)
#for review in neg_deceptive:
#print(classifier.classify(get_features(review,'negative')))
writeReviews("positive_polarity/deceptive_from_MTurk","posDec.txt")
writeReviews("positive_polarity/truthful_from_TripAdvisor","postru.txt")
writeReviews("negative_polarity/deceptive_from_MTurk","negDec.txt")
writeReviews("negative_polarity/truthful_from_Web","negtru.txt")
random.shuffle(posFeatureSet)
random.shuffle(negFeatureSet)
foldsize = 160
accuracyG = 0
accuracyD = 0
accuracyNGram = 0
accuracySentiment = 0
for x in range(10):
print(str(x)+" fold")
sentiTestSet = sentimentFeatures[x*foldsize:(x+1)*foldsize]
sentiTrainSet = sentimentFeatures[:(x-1)*foldsize]+sentimentFeatures[(x+1)*foldsize:]
nTestSet = nGramFeatures[x*foldsize:(x+1)*foldsize]
nTrainSet = nGramFeatures[:(x-1)*foldsize]+nGramFeatures[(x+1)*foldsize:]
testset = featureSets[x*foldsize:(x+1)*foldsize]
trainset = featureSets[:(x-1)*foldsize]+featureSets[(x+1)*foldsize:]
classifier = nltk.NaiveBayesClassifier.train(trainset)
sentiClassifier = nltk.NaiveBayesClassifier.train(sentiTrainSet)
nGramClassifier = nltk.NaiveBayesClassifier.train(nTrainSet)
accuracyNGram += nltk.classify.accuracy(nGramClassifier,nTestSet)
accuracyG += nltk.classify.accuracy(classifier,testset)
dTree = nltk.DecisionTreeClassifier.train(trainset)
accuracyD += nltk.classify.accuracy(dTree,testset)
accuracySentiment+=nltk.classify.accuracy(sentiClassifier,sentiTrainSet)
foldsize = 80
accuracyP = 0
for x in range(10):
posTestSet = posFeatureSet[x*foldsize:(x+1)*foldsize]
posTrainSet = posFeatureSet[:(x-1)*foldsize]+posFeatureSet[(x+1)*foldsize:]
pClassifier = nltk.NaiveBayesClassifier.train(posTrainSet)
accuracyP += nltk.classify.accuracy(pClassifier,posTestSet)
accuracyN = 0
for x in range(10):
negTestSet = posFeatureSet[x*foldsize:(x+1)*foldsize]
negTrainSet = posFeatureSet[:(x-1)*foldsize]+posFeatureSet[(x+1)*foldsize:]
nClassifier = nltk.NaiveBayesClassifier.train(negTrainSet)
accuracyN += nltk.classify.accuracy(nClassifier,negTestSet)
print("Generic : "+str(accuracyG/float(10)))
print("N-Classifier : "+str(accuracyN/float(10)))
print("P-Classifier : "+str(accuracyP/float(10)))
print("Decision Trees: "+str(accuracyD/float(10)))
print("N Gram :"+str(accuracyNGram/float(10)))
print("Sentiment Classifier : "+str(accuracySentiment/float(10)))