def main():
articles = CategorizedPlaintextCorpusReader(corpusdir, '.*', cat_pattern = r'(.*)[/]')
feats = {}
trainfeats = []
testfeats = []
for cat in articles.categories():
wow = len([f for f in articles.fileids(cat)]) # such variable name
print "for category", cat, ":", wow
feats[cat] = [(word_feats(articles.words(fileids = [f])), cat) for f in articles.fileids(cat)]
cutoff = wow - hold_back(wow)
trainfeats.append(feats[cat][:cutoff])
testfeats.append(feats[cat][cutoff:])
train = [item for sublist in trainfeats for item in sublist]
test = [item for sublist in testfeats for item in sublist]
print 'train on %d instances, test on %d instances' % (len(train), len(test))
classifier = NaiveBayesClassifier.train(train)
print 'accuracy:', nltk.classify.util.accuracy(classifier, test)
classifier.show_most_informative_features() # I don't understand the output for more than 2 categories :(
# load with:
# import pickle
# f = open('my_classifier.pickle')
# classifier = pickle.load(f)
# f.close()
with open('../data/classifier.pickle', 'wb') as f:
pickle.dump(classifier, f)
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
from nltk.tokenize.casual import TweetTokenizer
from normalization import normalizeTwitterWordsWithExtraFeatures, normalizeTwitterWordsWithNegationHandle
import pickle, nltk
tweetTokenizer = TweetTokenizer(reduce_len=True, preserve_case=True, strip_handles=False)
corpus = CategorizedPlaintextCorpusReader('corpus/2-step/polar', r'(\w+)-tweet[0-9]+\.txt', cat_pattern=r'(\w+)-tweet[0-9]+\.txt', word_tokenizer=tweetTokenizer)
normalizationFunction = normalizeTwitterWordsWithNegationHandle
wordsTaggedToCategory = []
i = 1
for category in corpus.categories():
for fileid in corpus.fileids(category):
words = corpus.words(fileids=[fileid])
normalizedWords = normalizationFunction(words)
extraNormalizedWords = normalizeTwitterWordsWithExtraFeatures(words)
wordsTagged = nltk.pos_tag(normalizedWords)
wordsTaggedToCategory += [(wordsTagged, category)]
print(i)
i += 1
with open("wordsTaggedToCategory-polar", 'wb') as fileout:
pickle.dump(wordsTaggedToCategory, fileout)
words = [w for w in words if not w.replace('.','',1).isdigit()]
if remove_odd_chars:
words = [re.sub(r'[^a-zA-Z0-9_]','_', w) for w in words]
if remove_stopwords:
sw = set(nltk.corpus.stopwords.words("english"))
words = [w for w in words if not w in sw]
if stem:
porter = nltk.PorterStemmer()
words = [porter.stem(w) for w in words]
return words
documents = [((fileid, category), preprocess(my_corpus.words(fileid),
to_lowercase = True, remove_punctuation = True, remove_digits = True,
remove_odd_chars = True, remove_stopwords=True, stem = False)) \
for category in my_corpus.categories() \
for fileid in my_corpus.fileids(category)]
def dummy_fun(doc):
return doc
bow_gen = sklearn.feature_extraction.text.CountVectorizer(
analyzer='word',
tokenizer=dummy_fun,
preprocessor=dummy_fun,
token_pattern=None,
reader = CategorizedPlaintextCorpusReader(d,
r'.*\.txt',
cat_pattern=r'(\w+)/*')
from textblob.classifiers import NaiveBayesClassifier
random.seed(1)
train = [
('Identity', 'IdentityThreat'),
('identity', 'IdentityThreat'),
('identities', 'IdentityThreat'),
('identity loss', 'IdentityThreat'),
('insider', 'InsiderThreat'),
('Malware', 'Malware'),
]
# Categorized corpora Reader collect the respective words based on ThreatType
ThreatTypes = [(list(reader.words(fileid)), category)
for category in reader.categories()
for fileid in reader.fileids(category)]
random.shuffle(ThreatTypes)
print(reader.categories())
new_train = ThreatTypes
print(new_train)
#Naive Bayes classifiers assume that the value of a particular feature is independent of the value of
#any other feature, given the class variable.
cl = NaiveBayesClassifier(train)
#update the classifier with training keywords from Categorized corpora
cl.update(new_train)
inputpath = nltk.data.find('corpora/abc/threatdescp.txt')
f = open(inputpath, encoding='latin2')
outputpath = nltk.data.find('corpora/abc/ResultNB.txt')
ResultFile = open(outputpath, 'w', encoding='latin2')
def getDirnames( path ) :
dirList = []
for f in os.listdir( path ) :
if not os.path.isfile( path ) :
if not f == ".DS_Store" :
dirList.append(f)
return dirList
###############################################
###############################################
#################
# TRAINING DATA #
#################
train_reader = CategorizedPlaintextCorpusReader('./training_data', r'.*\_.*\.txt', cat_pattern=r'.*\_(\w+)\.txt')
train_documents = [(list(train_reader.words(fileid)), category)
for category in train_reader.categories()
for fileid in train_reader.fileids(category)]
random.shuffle(train_documents)
#print train_documents
train_documents_clean = []
for i in train_documents :
cat = i[1]
#print cat
newList = []
for word in i[0] :
#print j
clean_word = word.encode('ascii', 'ignore').decode('ascii').encode('ascii', 'ignore')
newList.append(clean_word)
newTup = (newList, cat)
import nltk, random, string
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
from nltk.corpus import stopwords
reader = CategorizedPlaintextCorpusReader('./', r'.*\.txt', cat_pattern=r'(\w+)/*')
print reader.categories()
print reader.fileids()
documents = [(list(reader.words(fileid)), category)
for category in reader.categories()
for fileid in reader.fileids(category)]
random.shuffle(documents)
# Remove stopwords & punc from content
table = string.maketrans("","")
stopwords = nltk.corpus.stopwords.words('english')
filtered_words = [w for w in reader.words() if not w in stopwords]
filtered_words_nopunc = [w for w in filtered_words if not w in string.punctuation]
all_words = nltk.FreqDist(w.lower() for w in filtered_words_nopunc)
print all_words
word_features = all_words.keys()[:2000]
def document_features(document):
document_words = set(document)
features = {}
from textblob.classifiers import NaiveBayesClassifier
from nltk.corpus.reader import PlaintextCorpusReader, CategorizedPlaintextCorpusReader
from nltk.corpus import movie_reviews
import nltk
import random
from BeautifulSoup import BeautifulSoup
p = nltk.data.find('corpora/SecurityThreat-MaxEnt')
reader = CategorizedPlaintextCorpusReader(p,
r'.*\.txt',
cat_pattern=r'(\w+)/*')
from nltk import WordNetLemmatizer
#Using Wordnet Lemmatizer
wordnet_lemmatizer = WordNetLemmatizer()
all_words = nltk.FreqDist(word for word in reader.words())
top_words = list(all_words)[:100]
print(top_words)
def word_feats(words):
return {word: True for word in words if word in top_words}
#def word_feats(words):
#return dict([(wordnet_lemmatizer.lemmatize(word), True) for word in words])
# Generate all the files based on ThreatType.
IdentityThreat = reader.fileids('IdentityThreat')
InsiderThreat = reader.fileids('InsiderThreat')
Malware = reader.fileids('Malware')
loc = '/Users/rmoura/nltk_data/corpora/rai/textoSimples/'
corpus1 = PlaintextCorpusReader(loc, '.*\.txt')
print(corpus1.fileids())
print(corpus1.sents())
print(corpus1.words())
# Corpus texto etiquetado
from nltk.corpus.reader.tagged import TaggedCorpusReader
loc = '/Users/rmoura/nltk_data/corpora/rai/textoEtiquetas/'
corpus2 = TaggedCorpusReader(loc, '.*\.txt')
print(corpus2.fileids())
print(corpus2.words())
print("Palavras etiquetadas: ", corpus2.tagged_words())
print(corpus2.tagged_words('003.txt'))
print("Sentencas diretas:")
for s in corpus2.sents():
print(' '.join(s))
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
loc = '/Users/rmoura/nltk_data/corpora/rai/textoCategorias/'
corpus3 = CategorizedPlaintextCorpusReader(loc, '.*\.txt', cat_file="categorias.txt")
print(corpus3.fileids())
print(corpus3.categories())
print(corpus3.words(categories='brasnam'))
# Definicao de stopwords
stopwords = nltk.corpus.stopwords.words('portuguese')
fd = nltk.FreqDist(w.lower() for w in corpus3.words())
fd1 = nltk.FreqDist(w.lower() for w in corpus3.words()
if w.isalpha() and w not in stopwords)
class PolarityDataReader(object):
"""
PolarityDataReader:
Reader for POS/NEG Categorized Sentiword data
uses:
nltk.corpus.reader.CategorizedPlaintextCorpusReader
usage:
dataReader = PolarityDataReader([rootLocation],[readerObject])
dataReader.getDocuments()
dataReader.setTerms([No:ofTerms])
featuresets = dataReader.getTermDocMatrix()
"""
def __init__(self, rootLocation=config.POLARITY_DATASET, reader=None):
super(PolarityDataReader, self).__init__()
if reader == None:
self.reader = Reader(rootLocation,
r'.*/.*',
cat_pattern=r'(.*)/.*')
else:
self.reader = reader
self.setStopWords()
self.documents = None
self.terms = None
def getDocuments(self):
if not self.documents:
self.documents = [(list(self.reader.words(fileid)), category)
for category in self.reader.categories()
for fileid in self.reader.fileids(category)]
return self.documents
def setStopWords(self, fileLocation=config.STOP_WORDS_FILE):
stopfile = open(fileLocation, 'r')
self.stopwords = stopfile.read().split()
def removeStopWords(self, wordList):
""" Remove common words which have no search value """
return [word for word in wordList if word not in self.stopwords]
def setTerms(self, size=2000, featureSelection='PD', removeStopWords=True):
if featureSelection == 'PD':
self.__setTermsPD__(size)
print "Feature Selection : PD :done "
elif featureSelection == 'CHI_SQUARE':
self.__setTermsCHISQUARE__(size)
print "Feature Selection : CHI_SQUARE :done "
else:
"""
geting most frequent Words
"""
all_words = [w.lower() for w in self.reader.words()]
if removeStopWords:
all_words = self.removeStopWords(all_words)
all_words = FreqDist(w for w in all_words)
self.terms = all_words.keys()[:size]
print "Feature Selection: frequent Words :done "
def documentFeatures(self, document, sentiwordnet=False):
document_words = set(document)
features = {}
if sentiwordnet:
pass
#TODO
else:
for word in self.terms:
features[word] = (word in document_words)
return features
def getTermDocMatrix(self):
return [(self.documentFeatures(document), category)
for (document, category) in self.documents]
def __setTermsPD__(self, size):
"""
score=|(posDF-negDF)|/(posDF+negDF)
"""
posWord = {}
negWord = {}
for word in self.reader.words(categories=['pos']):
inc(posWord, word.lower())
for word in self.reader.words(categories=['neg']):
inc(negWord, word.lower())
wordScores = {}
for word in self.reader.words():
try:
posScore = posWord[word]
except KeyError, e:
posScore = 0
try:
negScore = negWord[word]
except KeyError, e:
negScore = 0
totalScore = posScore + negScore
if totalScore <= 10: # min total count
wordScores[word] = 0.1
else:
wordScore[word] = abs(posScore - negScore) / totalScore
#def tokenize(text):
# min_length = 3
# words = map(lambda word: word, word_tokenize(text));
# words = [word for word in words
# if word not in cachedStopWords]
# tokens =(list(map(lambda token: PorterStemmer().stem(token),
# words)));
# p = re.compile('[a-zA-Z]+');
# filtered_tokens = list(filter(lambda token:
# p.match(token) and len(token)>=min_length, tokens));
#
# return filtered_tokens
#Preparing a Tuple List of the Corpus Data based on
#Words In the corpus file and correspoindg category
data = [(list(tokenize(' '.join(reader.words(fileid)))), category)
for category in reader.categories()
for fileid in reader.fileids(category)]
#First preparing a train data set based on pre-identified features
featureListTrain = [
('Natural Language Processing', 'General'), ('Text Retrieval', 'General'),
('Text Access', 'General'), ('Information Retrieval', 'General'),
('NLP', 'General'), ('Content Analysis', 'General'),
('Vector', 'IR Models & Implementations'),
('Length', 'IR Models & Implementations'),
('Indexing', 'IR Models & Implementations'),
('Statistical', 'IR Models & Implementations'),
('Evaluation', 'IR Models- Evaluation,Ranking & Feedback'),
('Feedback', 'IR Models- Evaluation,Ranking & Feedback'),
def process_plaintext(dir_path):
reader = CategorizedPlaintextCorpusReader(dir_path,
r'.*\.txt',
cat_pattern=r'.+_.+_(.*)\.txt')
facilitator_files = reader.fileids(categories='facilitator')
participant_files = reader.fileids(categories='participant')
print facilitator_files, participant_files
#print reader.categories()
#print len(reader.words())
#print len(reader.sents())
fac_words = [word for word in reader.words(facilitator_files)]
par_words = [word for word in reader.words(participant_files)]
fac_words = edit_tokens(fac_words)
par_words = edit_tokens(par_words)
speakers = ([(word, 'facilitator')
for word in reader.words(facilitator_files)] +
[(word, 'participant')
for word in reader.words(participant_files)])
features = get_features(speakers)
size = int(len(features) * 0.3)
nb_train = features[size:]
nb_test = features[:size]
classifier = nltk.NaiveBayesClassifier.train(nb_train)
print "Classifier labels:", classifier.labels()
print classifier.show_most_informative_features()
print "Clasify test:", nltk.classify.accuracy(classifier, nb_test)
#print classifier.classify(get_features(["Yolo", "bag", "sp"], False))
#random.shuffle(speakers)
three_quarters = int(len(speakers) * 0.75)
train = speakers[:three_quarters]
test = speakers[three_quarters:]
est = lambda fdist, bins: nltk.probability.LaplaceProbDist(fdist)
un_lm = NgramModel(1, train, estimator=est)
bi_lm = NgramModel(2, train, estimator=est)
tr_lm = NgramModel(3, train, estimator=est)
qu_lm = NgramModel(4, train, estimator=est)
pe_lm = NgramModel(5, train, estimator=est)
print un_lm
print bi_lm
print tr_lm
print qu_lm
print pe_lm
print "1 gram Perplexity:", un_lm.perplexity(test)
print "2 gram Perplexity:", bi_lm.perplexity(test)
print "3 gram Perplexity:", tr_lm.perplexity(test)
print "4 gram Perplexity:", qu_lm.perplexity(test)
print "5 gram Perplexity:", pe_lm.perplexity(test)
print bi_lm.generate(10, ["uh", "sp"])
fd_fac = nltk.FreqDist(fac_words)
vocab_fac = fd_fac.keys()
fd_par = nltk.FreqDist(par_words)
vocab_par = fd_par.keys()
print "Fac Vocab: ", len(vocab_fac)
print "Fac Tokens: ", len(fac_words)
print vocab_fac[:20]
print "Par Vocab: ", len(vocab_par)
print "Par Tokens: ", len(par_words)
print vocab_par[:20]
fd_par.plot(50)
def process_plaintext(dir_path):
reader = CategorizedPlaintextCorpusReader(dir_path,
r'.*\.txt', cat_pattern=r'.+_.+_(.*)\.txt')
facilitator_files = reader.fileids(categories='facilitator')
participant_files = reader.fileids(categories='participant')
print facilitator_files, participant_files
#print reader.categories()
#print len(reader.words())
#print len(reader.sents())
fac_words = [word for word in reader.words(facilitator_files)]
par_words = [word for word in reader.words(participant_files)]
fac_words = edit_tokens(fac_words)
par_words = edit_tokens(par_words)
speakers = (
[(word, 'facilitator') for word in reader.words(facilitator_files)] +
[(word, 'participant') for word in reader.words(participant_files)]
)
features = get_features(speakers)
size = int(len(features) * 0.3)
nb_train = features[size:]
nb_test = features[:size]
classifier = nltk.NaiveBayesClassifier.train(nb_train)
print "Classifier labels:", classifier.labels()
print classifier.show_most_informative_features()
print "Clasify test:", nltk.classify.accuracy(classifier, nb_test)
#print classifier.classify(get_features(["Yolo", "bag", "sp"], False))
#random.shuffle(speakers)
three_quarters = int(len(speakers) * 0.75)
train = speakers[:three_quarters]
test = speakers[three_quarters:]
est = lambda fdist, bins: nltk.probability.LaplaceProbDist(fdist)
un_lm = NgramModel(1, train, estimator=est)
bi_lm = NgramModel(2, train, estimator=est)
tr_lm = NgramModel(3, train, estimator=est)
qu_lm = NgramModel(4, train, estimator=est)
pe_lm = NgramModel(5, train, estimator=est)
print un_lm
print bi_lm
print tr_lm
print qu_lm
print pe_lm
print "1 gram Perplexity:", un_lm.perplexity(test)
print "2 gram Perplexity:", bi_lm.perplexity(test)
print "3 gram Perplexity:", tr_lm.perplexity(test)
print "4 gram Perplexity:", qu_lm.perplexity(test)
print "5 gram Perplexity:", pe_lm.perplexity(test)
print bi_lm.generate(10, ["uh", "sp"])
fd_fac = nltk.FreqDist(fac_words)
vocab_fac = fd_fac.keys()
fd_par = nltk.FreqDist(par_words)
vocab_par = fd_par.keys()
print "Fac Vocab: " , len(vocab_fac)
print "Fac Tokens: " , len(fac_words)
print vocab_fac[:20]
print "Par Vocab: " , len(vocab_par)
print "Par Tokens: " , len(par_words)
print vocab_par[:20]
fd_par.plot(50)
#set up path to data
data_folder_name = sys.argv[1]
data_path = os.path.join(os.getcwd(), '', data_folder_name)
#make article object to read in files
article = CategorizedPlaintextCorpusReader(data_path,
r'.*\.*\.txt',
cat_pattern=r'(\w+).*\.txt')
#make list of all articles with labels based on what folder the file is in
all_articles = []
for category in article.categories():
for fileid in article.fileids(category):
#lowercases words and takes out stopwords
process = list(
w.lower() for w in list(article.words(fileid))
if w.isalpha() and w not in stopwords.words('english'))
entry = [process, category]
all_articles.append(entry)
random.shuffle(all_articles)
#make bigrams for every article
word_bigrams = [(nltk.bigrams(all_articles[i][0]))
for i in range(len(all_articles))]
#create frequency distribution for all words and select top 2000 for features
all_words = nltk.FreqDist(article.words())
word_features = list(all_words)[:2000]
#create list holding all bigrams
def generate_model(cfdist, word, num=15):
for i in range(num):
print(word, end=' ')
word = cfdist[word].max()
# 1. Construir Corpus texto categorizado
locPT = 'ch02/ES'
corpusPT = CategorizedPlaintextCorpusReader(locPT,
'.*\.txt',
cat_file="cat.txt")
print(corpusPT.fileids())
print(corpusPT.categories())
print(corpusPT.words(categories='ciencia'))
#print(corpusPT.raw())
vocab = set(w.lower() for w in corpusPT.words())
print('Tamanho Vocabulario:', len(vocab))
corpusCom = corpusPT.raw()
corpusComList = corpusCom.split()
print('Tamanho Total de palabras:', len(corpusComList))
# 2. Calcular medidas estadisticas simples
'''
Medidas: Tamanho médio das palavras, Tamanho médio das sentenças e Número de vezes que cada
item do vocabulário aparece no texto em média (escore de diversidade léxica)
'''
print(
'Tamanho médio das palavras/Tamanho médio das sentenças/Escore de diversidade léxica'
class PolarityDataReader(object):
"""
PolarityDataReader:
Reader for POS/NEG Categorized Sentiword data
uses:
nltk.corpus.reader.CategorizedPlaintextCorpusReader
usage:
dataReader = PolarityDataReader([rootLocation],[readerObject])
dataReader.getDocuments()
dataReader.setTerms([No:ofTerms])
featuresets = dataReader.getTermDocMatrix()
"""
def __init__(self, rootLocation = config.POLARITY_DATASET,reader=None):
super(PolarityDataReader, self).__init__()
if reader == None:
self.reader = Reader(rootLocation,r'.*/.*', cat_pattern=r'(.*)/.*')
else:
self.reader = reader
self.setStopWords()
self.documents = None;
self.terms = None;
def getDocuments(self):
if not self.documents:
self.documents = [(list(self.reader.words(fileid)), category)
for category in self.reader.categories()
for fileid in self.reader.fileids(category)]
return self.documents;
def setStopWords(self,fileLocation = config.STOP_WORDS_FILE):
stopfile = open(fileLocation, 'r')
self.stopwords = stopfile.read().split()
def removeStopWords(self,wordList):
""" Remove common words which have no search value """
return [word for word in wordList if word not in self.stopwords]
def setTerms(self,size=2000,featureSelection='PD',removeStopWords=True):
if featureSelection == 'PD':
self.__setTermsPD__(size)
print "Feature Selection : PD :done "
elif featureSelection == 'CHI_SQUARE':
self.__setTermsCHISQUARE__(size)
print "Feature Selection : CHI_SQUARE :done "
elif featureSelection == 'SWNSS':
self.__setTermsSWNSS__(size)
print "Feature Selection : SWNPD :done "
else:
"""
geting most frequent Words
"""
all_words = [w.lower() for w in self.reader.words()];
if removeStopWords:
all_words = self.removeStopWords(all_words);
all_words = FreqDist(w for w in all_words)
self.terms = all_words.keys()[:size]
print "Feature Selection: frequent Words :done "
def documentFeatures(self,document,sentiwordnet=False):
document_words = set(document)
features = {}
if sentiwordnet:
pass
#TODO
else :
for word in self.terms:
features[word] = (word in document_words)
return features
def getTermDocMatrix(self):
return [(self.documentFeatures(document), category)
for (document,category) in self.documents]
def __setTermsPD__(self,size):
"""
score=|(posDF-negDF)|/(posDF+negDF)
"""
posWord = {};
negWord = {};
for word in self.reader.words(categories = ['pos']):
inc(posWord,word.lower());
for word in self.reader.words(categories = ['neg']):
inc(negWord,word.lower());
wordScores = {}
for word in self.reader.words():
try:
posScore = posWord[word]
except KeyError, e:
posScore = 0
try:
negScore = negWord[word]
except KeyError, e:
negScore = 0
totalScore = posScore + negScore
if totalScore <= 10 : # min total count
wordScores[word] = 0.1
else :
wordScores[word] = abs(posScore-negScore)/totalScore
#removeStopWords does no affect accurcy
termScore = sorted(wordScores.items(),key=lambda(w,s):s,reverse=True)[:size]
self.terms = [w for (w,s) in termScore];
from nltk.corpus.reader import CategorizedPlaintextCorpusReader
from nltk.corpus import stopwords
bigram_measures = nltk.collocations.BigramAssocMeasures()
#print reader.categories()
for name in os.listdir("."):
if os.path.isdir(name):
reader = CategorizedPlaintextCorpusReader(name, r'.*\.txt', cat_pattern=r'(\w+)/*')
# reader = CategorizedPlaintextCorpusReader(name, r'./raw_reviews/\.txt', cat_pattern=r'(\w+)/*')
print reader.fileids()
table = string.maketrans("","")
stopwords = nltk.corpus.stopwords.words('english')
filtered_words = [w for w in reader.words() if not w in stopwords]
filtered_words_nopunc = [w for w in filtered_words if not w in string.punctuation]
#all_words = nltk.FreqDist(w.lower() for w in filtered_words_nopunc)
finder = BigramCollocationFinder.from_words(filtered_words_nopunc)
#scored = finder.score_ngrams(bigram_measures.raw_freq)
#a = sorted(bigram for bigram, score in scored)
finder.apply_freq_filter(3)
a = finder.nbest(bigram_measures.pmi, 5)
#b = finder.score_ngrams(bigram_measures.pmi)
print a
#documents = [(list(reader.words(fileid)), category)
# for category in reader.categories()
# for fileid in reader.fileids(category)]
file.write((str(cat)))
file.write("\t\t")
file.write(str(Feature_Set[word,cat]))
file.write("\n")
file.close()
Classification_Accuracy=0
for file in Testing_Corpus.fileids():
pos_prob=1
neg_prob=1
real_category=Testing_Corpus.categories([file])
for word, cat in Feature_Set:
if word in Testing_Corpus.words([file]):
if cat=="pos":
pos_prob=Feature_Set[word, cat]*float(pos_prob)*10000
else:
neg_prob=Feature_Set[word, cat]*float(neg_prob)*10000
if float(pos_prob)>=float(neg_prob):
derived_category="['pos']"
else:
derived_category="['neg']"
if str(real_category)==str(derived_category):
Classification_Accuracy=Classification_Accuracy + 1
print "Feature set is stored in model_file"
print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
classifier.show_most_informative_features()
def word_feats(words):
return dict([(word, True) for word in words])
print 'evaluating single word features'
evaluate_classifier(word_feats)
word_fd = FreqDist()
label_word_fd = ConditionalFreqDist()
for word in imdb_reviews.words(categories=['pos']):
word_fd[word.lower()] += 1
label_word_fd['pos'][word.lower()] += 1
for word in imdb_reviews.words(categories=['neg']):
word_fd[word.lower()] += 1
label_word_fd['neg'][word.lower()] += 1
# n_ii = label_word_fd[label][word]
# n_ix = word_fd[word]
# n_xi = label_word_fd[label].N()
# n_xx = label_word_fd.N()`
pos_word_count = label_word_fd['pos'].N()
neg_word_count = label_word_fd['neg'].N()
total_word_count = pos_word_count + neg_word_count
cat_pattern = r'(\w+)/*') # file name format
# Positive reviews file ids
pos_ids = reader.fileids('pos')
# Negative reviews file ids
neg_ids = reader.fileids('neg')
'''Generating word feature list'''
def word_feats(words):
return dict([(word, True) for word in words])
'''Building positive and negative feature lists. Each
item is the positive/negative word features for a review file'''
pos_feat = [(word_feats(reader.words(fileids = f)), 'pos')
for f in pos_ids]
neg_feat = [(word_feats(reader.words(fileids = f)), 'neg')
for f in neg_ids]
'''refining feature lists, stemming, removing punctuation and stop words from pos_feat'''
pos_feat = []
import re
for file in pos_ids[:15000]:
# reset review variable
review = ''
# Create a string of the text in the file
review = ' '.join(word for word in
reader.words(fileids = [file]))
