def tag(self, corpus, tokenize=True):
'''Tags a string `corpus`.'''
# Assume untokenized corpus has \n between sentences and ' ' between words
s_split = SentenceTokenizer(
).tokenize if tokenize else lambda t: t.split('\n')
w_split = WordTokenizer().tokenize if tokenize else lambda s: s.split()
def split_sents(corpus):
for s in s_split(corpus):
yield w_split(s)
prev, prev2 = self.START
tokens = []
for words in split_sents(corpus):
context = self.START + [self._normalize(w)
for w in words] + self.END
for i, word in enumerate(words):
tag = self.tagdict.get(word)
if not tag:
features = self._get_features(i, word, context, prev,
prev2)
tag = self.model.predict(features)
tokens.append((word, tag))
prev2 = prev
prev = tag
return tokens
def extract_global_bag_of_words_processed(df_comments):
corpus = []
i = 0
lemmatizer = WordNetLemmatizer()
tb = Blobber(pos_tagger=PerceptronTagger())
sentencer = SentenceTokenizer()
for _,row in df_comments.iterrows():
comm = row['comment_content']
tokens = []
for sent in sentencer.tokenize(comm.decode('ascii','ignore')):
tagged = tb(sent.lower()).tags
# Remove stops
filtered_words = [w for w in tagged if not w[0] in stopwords.words('english')]
# Remove punctuation
filtered_words = [(re.findall('[a-z]+', w[0].lower())[0], w[1]) for w in filtered_words if len(re.findall('[a-z]+', w[0].lower())) > 0]
# Lemmatize
filtered_words = [lemmatizer.lemmatize(w[0], penn_to_wn(w[1])) for w in filtered_words]
filtered_words = [w for w in filtered_words if len(w) > 1]
for word in filtered_words:
tokens.append(word)
corpus.append(' '.join(tokens))
i += 1
if i % 1000 == 0:
print i, "words processed for Ngrams"
return corpus
def _create_sentence_objects(self):
'''Returns a list of Sentence objects given
a list of sentence strings. Attempts to handle sentences that
have more than one punctuation mark at the end of the sentence.
Examples: "An ellipses is no problem..." or "This is awesome!!!"
'''
sent_tokenizer = SentenceTokenizer()
sentence_objects = []
sentences = sent_tokenizer.itokenize(self.raw)
char_index = 0 # Keeps track of character index within the blob
for sent in sentences:
# Compute the start and end indices of the sentence
# within the blob
start_index = self.raw.index(sent, char_index)
char_index += len(sent)
end_index = start_index + len(sent)
# Sentences share the same models as their parent blob
s = Sentence(sent,
start_index=start_index,
end_index=end_index,
tokenizer=self.tokenizer,
np_extractor=self.np_extractor,
pos_tagger=self.pos_tagger,
analyzer=self.analyzer,
parser=self.parser,
classifier=self.classifier)
sentence_objects.append(s)
return sentence_objects
def comment_to_sentences(comment, remove_stops=False):
sentencer = SentenceTokenizer()
corpus = []
for sent in sentencer.tokenize(comment):
if len(sent) > 0:
corpus.append(comment_to_wordlist(sent, remove_stops))
return corpus
def test_overrides(self):
b = tb.Blobber(tokenizer=SentenceTokenizer(),
np_extractor=ConllExtractor())
blob = b("How now? Brown cow?")
assert_true(isinstance(blob.tokenizer, SentenceTokenizer))
assert_equal(blob.tokens, tb.WordList(["How now?", "Brown cow?"]))
blob2 = b("Another blob")
# blobs have the same tokenizer
assert_true(blob.tokenizer is blob2.tokenizer)
# but aren't the same object
assert_not_equal(blob, blob2)
def test_get_np_for_CONLLExtractor(self):
text_list = self.text_list
from textblob.taggers import NLTKTagger
from textblob.tokenizers import SentenceTokenizer
chunker = ConllExtractor()
tb = Blobber(pos_tagger=NLTKTagger(),
tokenizer=SentenceTokenizer(),
np_extractor=chunker)
for text in text_list:
b = tb(text)
print(b.noun_phrases)
print(b.parse())
def plagiarism_check(reader, pdfurl):
global cred_score
text = ''
for i in range(5, reader.numPages):
text += reader.getPage(i).extractText()
sentences = TextBlob(text, tokenizer=SentenceTokenizer())
sentences = [' '.join(sentence.split()) for sentence in sentences]
sentences = [sentence for sentence in sentences if len(sentence) > 50]
t = random.sample(sentences, min(len(sentences), 3)) # can increase this number
for sentence in t:
print(sentence)
res = requests.get('https://www.google.ca/search?q="' + sentence + '"')
soup = bs4.BeautifulSoup(res.text, 'html.parser')
results = soup.select('h3.r a')
for result in results[:min(len(results), 3)]: # can increase this number
if results.get('href') != pdfurl:
cred_score -= 0.05
output['plagiarism'] = -0.05
return
def test_get_np_for_all(self):
text_list = self.text_list
from textblob.taggers import NLTKTagger
from textblob.tokenizers import SentenceTokenizer
chunker = ConllExtractor()
tb = Blobber(pos_tagger=NLTKTagger(),
tokenizer=SentenceTokenizer(),
np_extractor=chunker)
for text in text_list:
# tbinstance=tb(text)
# sentences=tbinstance.sentences
# print(sentences)
# for s in sentences:
# s.
pst = parsetree(text)
print(pst)
for sentence in pst:
for chunk in sentence.chunks:
if chunk.type == "NP":
print chunk.type, [(w.string, w.type)
for w in chunk.words]
def setUp(self):
self.tokenizer = SentenceTokenizer()
self.text = "Beautiful is better than ugly. Simple is better than complex."
import string
from FeatureExtraction.mainExtractor import CharacterAnalyzer
from textblob.tokenizers import SentenceTokenizer, WordTokenizer
sentencer = SentenceTokenizer()
worder = WordTokenizer()
sentences = ['How are you? I am fine!']
tokens = []
for sent in sentencer.tokenize(sentences[0].lower()):
words = ''.join([ch for ch in sent if ch not in string.punctuation])
words = worder.tokenize(words)
for word in words:
tokens.append(word.strip())
if len(word) > 2:
for j in range(0, len(word)):
term = word[:j] + word[j + 1:]
tokens.append(term.strip())
print tokens
def __init__(self):
self.lemmatizer = WordNetLemmatizer()
self.tb = Blobber(pos_tagger=PerceptronTagger())
self.sentencer = SentenceTokenizer()
def __init__(self):
self.sentencer = SentenceTokenizer()
self.max = 8
self.min = 2
def __init__(self):
self.sentencer = SentenceTokenizer()
self.worder = WordTokenizer();
def extract_feature_matrix(df_comments, df_thread_groupby):
print "START"
# Sentence Tokenizer
sentencer = SentenceTokenizer()
clf = load_classifier(sentiment_path + 'sentiment_classifier.pickle')
featureMatrix = np.empty([df_comments.shape[0],25])
feature_dict = dict()
for ix, row in df_comments.iterrows():
feature_dict[row['comment_id']] = ix
feature_count = 0
for _,row in df_comments.iterrows():
index = feature_dict[row['comment_id']]
comm = row['comment_content'].decode('ASCII', 'ignore')
tokens = words(comm)
unique_tokens = set(tokens)
sentences = sentencer.tokenize(comm)
featureMatrix[index][3] = len(comm)
verb_fr, noun_fr, pronoun_fr = pos_freq(tokens)
featureMatrix[index][4] = verb_fr
featureMatrix[index][5] = noun_fr
featureMatrix[index][6] = pronoun_fr
featureMatrix[index][7] = capital_frequency(tokens)
featureMatrix[index][8] = sent_frequency(sentences, '?')
featureMatrix[index][9] = sent_frequency(sentences, '!')
featureMatrix[index][10] = sentence_capital_frequency(sentences)
featureMatrix[index][11] = entropy(comm)
featureMatrix[index][12] = lexical_diversity(tokens)
if len(tokens) == 0:
featureMatrix[index][13] = 0
featureMatrix[index][14] = 0
featureMatrix[index][15] = 0
featureMatrix[index][16] = 0
else:
spelt_wrong = missing_words(unique_tokens)
bad_words_list = swears(unique_tokens)
featureMatrix[index][13] = len(spelt_wrong)
featureMatrix[index][14] = len(spelt_wrong)/float(len(unique_tokens))
featureMatrix[index][15] = len(bad_words_list)
featureMatrix[index][16] = len(bad_words_list)/float(len(unique_tokens))
featureMatrix[index][19] = F_K_score(sentences, tokens)
testSet = dict()
refWords = make_full_dict(tokens)
testSet.update(refWords)
probDist = clf.prob_classify(testSet)
sentiment = probDist.prob('pos')
subj_obj = get_subjectivity(probDist)
polarity_overlap = get_polarity_overlap(words(row['article_body']), tokens, clf)
featureMatrix[index][22] = sentiment
featureMatrix[index][23] = subj_obj
featureMatrix[index][24] = polarity_overlap
feature_count += 1
if feature_count % 1000 == 0:
print feature_count
print "DONE"
feature_count = 0
# Grouped
for _,group in df_thread_groupby:
thread_comments = [row['comment_content'] for _,row in group.iterrows()]
# Get average time
sumTime = 0
count = 0
previous = mktime(group.iloc[0]['date'])
first = mktime(group.iloc[0]['date'])
# Average length
sumLen = 0
thread_tokens = []
# Within Thread
for _, row in group.iterrows():
index = feature_dict[row['comment_id']]
comm = row['comment_content'].decode('ascii','ignore')
tokens = words(comm)
sentences = sentencer.tokenize(comm)
# Ongoing average time
sumTime += mktime(row['date']) - previous
count += 1
avgTime = sumTime/float(count)
# Ongoing average length
sumLen += len(words(row['comment_content']))
avgLen = sumLen/float(count)
######################################################################
# Get chunked sentences
for sent in sentences:
sent_tokens = words(sent)
sent_tokens_tagged = nltk.pos_tag(sent_tokens)
chunks = nltk.ne_chunk(sent_tokens_tagged, binary=True)
doc = []
for chunk in chunks:
if type(chunk) == nltk.Tree:
doc.append(' '.join(c[0] for c in chunk.leaves()))
else:
doc.append(chunk[0])
doc = [word.strip(string.punctuation) for word in doc if len(word.strip(string.punctuation)) > 1]
# The cumulative tokens up to this point
thread_tokens += doc
######################################################################
article_tokens = []
article_sentences = sentencer.tokenize(row['article_body'])
for sent in article_sentences:
sent_tokens = words(sent)
sent_tokens_tagged = nltk.pos_tag(sent_tokens)
chunks = nltk.ne_chunk(sent_tokens_tagged, binary=True)
doc = []
for chunk in chunks:
if type(chunk) == nltk.Tree:
doc.append(' '.join(c[0] for c in chunk.leaves()))
else:
doc.append(chunk[0])
article_tokens = [word.strip(string.punctuation) for word in doc if len(word.strip(string.punctuation)) > 1]
######################################################################
featureMatrix[index][0] = timeliness(mktime(row['date']), previous, max(avgTime, 1))
previous = mktime(row['date'])
featureMatrix[index][1] = mktime(row['date']) - first
featureMatrix[index][2] = lengthiness(words(row['comment_content']), max(avgLen, 1))
featureMatrix[index][17] = np.mean([termf(comm.count(w), tokens) for w in set(tokens)])
featureMatrix[index][18] = tf_idf(comm, thread_comments)
featureMatrix[index][20] = onSubForumTopic(tokens, thread_tokens)
featureMatrix[index][21] = onSubForumTopic(tokens, article_tokens)
feature_count += 1
if feature_count % 1000 == 0:
print feature_count
return featureMatrix
