def make_pos(target_tag, edit_rev):
tags, srcs, dsts = edit_rev_triple
# target_tag: 文中に存在する
# 品詞を付与する前に、文中から削除・追加タグが存在する部分を取り除く
if target_tag == del_tag:
sentence = dsts
elif target_tag == add_tag:
sentence = srcs
if target_tag in tags:
tag_indexes = [i for i, x in enumerate(tags) if x == target_tag]
trimed = sentence
for tag_index in tag_indexes:
trimed = trimed[:tag_index] + trimed[tag_index+1:]
posed = pos_tag(trimed)
pos = [w[1] for w in posed]
for tag_index in tag_indexes:
pos.insert(tag_index, u'')
#debug
None_indexes = [i for i, x in enumerate(pos) if x == u'']
if tag_indexes != None_indexes:
print >>sys.stderr, tag_indexes
print >>sys.stderr, None_indexes
print >>sys.stderr, tags
print >>sys.stderr, pos
else:
posed = pos_tag(u' '.join(sentence).split())
pos = [w[1] for w in posed]
return pos
def number_of_exact_word_match(a, b, word_tokenizer, lemmatizer, stop_words):
pos_a = map(get_tagged_words, pos_tag(word_tokenizer.tokenize(a)))
pos_b = map(get_tagged_words, pos_tag(word_tokenizer.tokenize(b)))
lemmae_a = [lemmatizer.lemmatize(token.lower().strip(punctuation), pos) for token, pos in pos_a \
if token.lower().strip(punctuation) not in stop_words]
lemmae_b = [lemmatizer.lemmatize(token.lower().strip(punctuation), pos) for token, pos in pos_b \
if token.lower().strip(punctuation) not in stop_words]
matched_words = set(lemmae_a).intersection(lemmae_b)
return [len(matched_words), matched_words, b]
def number_of_noun_match(a, b, word_tokenizer, lemmatizer, stop_words):
pos_a = map(get_tagged_words, pos_tag(word_tokenizer.tokenize(a)))
pos_b = map(get_tagged_words, pos_tag(word_tokenizer.tokenize(b)))
lemmae_a = [lemmatizer.lemmatize(token.lower().strip(punctuation), pos) for token, pos in pos_a \
if pos == NOUN and token.lower().strip(punctuation) not in stop_words]
lemmae_b = [lemmatizer.lemmatize(token.lower().strip(punctuation), pos) for token, pos in pos_b \
if pos == NOUN and token.lower().strip(punctuation) not in stop_words]
# Calculate Jaccard similarity
#ratio = len(set(lemmae_a).intersection(lemmae_b)) / float(len(set(lemmae_a).union(lemmae_b)))
#return (ratio > 0.66)
matched_words = set(lemmae_a).intersection(lemmae_b)
return [len(matched_words), matched_words, b]
def _process_simpleHash(self, simpleHash):
# Extract entities from keys resulting from SimpleExtractor process_*
entityHash = {}
for data in simpleHash:
occs = simpleHash[data]['occurences']
proxLoc = simpleHash[data]['proxLoc']
# Tokenize sentences
for sent in tokenize_sentences(data):
# Tokenize words
tokens = tokenize_words(sent)
# Tag words with Parts of Speech
tagged = pos_tag(tokens)
# Identify named entities
entities = ne_chunk(tagged)
for ent in entities:
if isinstance(ent, NLTKParseTree):
# Is it a wanted type?
if ent.node in self.types:
# Should we keep the PoS tag?
if self.keepPos:
txts = ['/'.join(token) for token in ent.leaves()]
else:
txts = [token[0] for token in ent.leaves()]
txt = ' '.join(txts)
new = {txt: {'text': txt,
'occurences': occs,
'proxLoc': proxLoc[:]}}
entityHash = self._mergeHash(entityHash, new)
return entityHash
def parse(body):
contents = []
if isinstance(body, basestring):
contents.append(body)
else:
contents = body
sentences = []
for content in contents:
sentences.extend([sentence for sentence in sent_tokenize(content) if not str_helper.hasHTMLTag(sentence)])
stop = stopword.get_stopwords()
tokens = {}
for sentence in sentences:
for word in word_tokenize(sentence.lower()):
if word not in stop and not str_helper.hasNumbers(word) and not str_helper.hasPunctuation(word):
word = stem.stemming(word)
tokens.setdefault(word, 0)
tokens[word] += 1
wp = pos_tag(tokens.keys())
words = [row[0] for row in wp]
tags = [row[1] for row in wp]
return words, tags
def extract_onlynouns(tokens):
out = list()
for token in tokens:
pos = pos_tag(nltk.word_tokenize(token.lower()))[0][1]
if (pos == "NN") or (pos == "NNP"):
out.append(token)
return out
def get_final_query_with_tokens(self, query):
""" Get final query with keywords selected and processed
Parameters
----------
query : string, mandatory
The initial query
"""
result = {};
result['query'] = query
words = pos_tag(nltk.word_tokenize(query))
#grammar = "NP: {<DT>?<JJ>*<NN>}"
#cp = nltk.RegexpParser(grammar)
#tree = cp.parse(words);
#print(tree)
#NPs = list(tree.subtrees(filter=lambda x: x.label()=='NP' or x.label().startswith('NN') or x.label()=='WP'))
#print [' '.join(NP.leaves()[0]) for NP in NPs ]
#print [(word, pos) for word,pos in words if pos.startsWith('NN') or pos == 'WP']
result['select'] = self.get_select_tokens(words)
# return query and required tokens
return result
#qs = QueryTokenizer()
#inputs = ['s', 'what type of persons travels ?', 'who are travelling ?', 'what is the tour schedule ?', 'what is the cost for a tour?']
#for q in inputs:
# print qs.get_final_query_with_tokens(q)
def main():
wsjsubset = open("../corpus/wsjsubset", 'r').readlines()
genia = open("../corpus/genia", 'r').readlines()
words = []
postag = []
chunktag = []
for line in wsjsubset:
if len(line.split()) > 0:
words.append(line.split()[0])
postag.append(line.split()[1])
chunktag.append(line.split()[2])
postag_nltk = pos_tag(words)
hits = 0
fails = {}
for i in xrange(len(postag_nltk)):
if postag_nltk[i][1] == postag[i]:
hits += 1
else:
fails[(postag[i], postag_nltk[i][1])] = fails.get((postag[i], postag_nltk[i][1]), 0) + 1
accuracy = hits/float(len(postag))
for fail in fails:
fails[fail] = fails[fail] / float(len(postag) - hits)
for key, value in fails.iteritems():
print value, key
def extract_entities(words):
entities = []
for chunk in ne_chunk(pos_tag(words)):
if hasattr(chunk, 'node'):
performer = ' '.join(c[0] for c in chunk.leaves())
entities.append(performer.lower())
return entities
def tagging_ranks(theList):
importantWords = []
upTo = len(theList)
for i in range(0, upTo):
print theList[i][0]
poS = tag.pos_tag(tokenize.word_tokenize(theList[i][0]))
print poS
theList[i].append(poS[0][1])
if theList[i][1]<4:
break
print theList
for words in theList:
if len(words) > 2:
if words[2].startswith(('JJ', 'NN', 'RB', 'VB')):
importantWords.append(words)
print importantWords
return "Passed"
pass
def process_raw_text(text):
"""
First some code to standardize the formatting, then basic nlp.
"""
# Remove breaks and tabs
for char in ["\t", "\n"]:
text = text.replace(char, " ")
text = text.replace('."', '".')
text = text.replace(".'", "'.")
# Split special characters from words
for char in ["'", '"', ",", ".", "?", "!", ";", ":"]:
text = text.replace(char, " " + char + " ")
# Magic to remove all multi-spaces
text = ' '.join(text.split())
# get the words, sentences, POS tags, and chunks.
chunks = [ tuple([ c.type for c in t.chunks ]) for t in parsetree(text) ]
sentences = sent_tokenize(text)
sentences = [ word_tokenize(s) for s in sentences ]
sentences_tags = [ tuple([ (w, simplify_tag(t)) for w, t in pos_tag(s) ]) for s in sentences ]
sentences = [ tuple([ w for w, _ in s]) for s in sentences_tags ]
tags = [ tuple([ t for _, t in s]) for s in sentences_tags ]
words = flatten(sentences)
return tuple(words), tuple(sentences), tuple(tags), tuple(chunks)
def title_permutations(title_expanded):
title_tagged = pos_tag(title_expanded.split())
st = PorterStemmer()
title_pos = [st.stem(word) for word, pos in title_tagged if pos != 'IN']
title_perms = list(map("*".join, permutations(title_pos)))
return title_perms
def processoFeatures(resposta):
frases = tokenizerFrases.tokenize(resposta["corpo"])
palavras = []
palavrasTexto = {}
for frase in frases:
palavrasTemp = tokenizerPalavras.tokenize(frase)
for palavra in palavrasTemp:
palavrasTexto[palavra] = True
posTags = pos_tag(palavras)
positivo = 0
negativo = 0
for palavra, tag in posTags:
synsets = None
if tag.startswith("J"):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.ADJ)
elif tag.startswith("V"):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.VERB)
elif tag.startswith("N"):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.NOUN)
elif tag.startswith("R"):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.ADV)
else:
synsets = sentiwordnet.senti_synsets(palavra, "")
if synsets != None:
synsets = list(synsets)
if len(synsets) > 0:
synset = synsets[0]
positivo = positivo + synset.pos_score()
negativo = negativo + synset.neg_score()
if positivo > negativo:
return (palavrasTexto, "positivo")
elif negativo > positivo:
return (palavrasTexto, "negativo")
else:
return (palavrasTexto, "neutro")
def GetContractPage(x):
url = 'http://www.defense.gov/contracts/contract.aspx?contractid=%d' % x
html = urllib.urlopen(url).read()
if re.search("The Official Home of the Department of Defense", html):
return
soup = BeautifulSoup(html)
p_tags = soup.findAll("p")
p_tags_text_list = [tag.text for tag in p_tags]
tokenized_list = []
for text in p_tags_text_list:
tokenized_list = tokenize.word_tokenize(text)
tokenized_list.append(nltk_tag.pos_tag(tokenized_list))
tagged_list = tokenized_list[-1]
data = {
"url": url}
for token in tagged_list[1:]:
if token[1]=="NNP":
data['entity'] = token[0]
break
for token in tagged_list[1:]:
if token[1]=="CD":
data['Amount'] = token[0]
break
print data
def test_run():
results = {}
nouns = []
product_list = {}
for p in Post.query.all():
tagged_sent = pos_tag(p.story.split())
propernouns = [word for word,pos in tagged_sent if pos == 'NNP']
for n in propernouns:
if n == "I’m" or n == "It’s" or n == "Can’t":
continue
results[n.replace('.', '')] = True
for r in results.keys():
nouns.append(r)
for i in range(10):
noun = random.choice(nouns)
# print('Using "%s"', (noun,))
for k in test_keywords:
try:
products = amazon.search(Keywords=noun, SearchIndex=k)
for product in products:
product_list[product.title] = True
except:
continue
for p in product_list.keys():
print(" Found title: %s" % (p,))
def count_words_unigram_pos(input_filename, output_path=''):
txt = get_file_text(input_filename)
word_regex = '[a-zA-Z]+'
word_frequency = {}
total_words = 0.
matches = re.findall(word_regex, txt, re.M + re.S + re.U)
for m in matches:
word_frequency[m] = word_frequency.get(m, 0.) + 1.
total_words+=1.
sorted_words = sorted(word_frequency.iteritems(), key=operator.itemgetter(1))
word_analysis = []
for word in sorted_words:
pos = pos_tag([word[0]])
word_analysis.append([word[0], word[1], pos[0][1]])
o_file = make_output_file(input_filename, output_path=output_path, prefix='', suffix='-words_unigram_pos')
o_file.write('word\tcount\tpos\n')
for w in word_analysis:
o_file.write('%s\t%d\t%s\n' % (w[0], w[1], w[2]))
o_file.close()
def generate_searches(posts):
results = {}
nouns = []
for p in posts:
tagged_sent = pos_tag(p.story.split())
propernouns = []
last_noun = False
for word,pos in tagged_sent:
if pos == 'NNP':
if last_noun:
propernouns[-1] = propernouns[-1] + ' ' + word
else:
propernouns.append(word)
last_noun = True
else:
last_noun = False
for n in propernouns:
if n == "I’m" or n == "It’s" or n == "Can’t":
continue
results[n.replace('.', '')] = True
for r in results.keys():
nouns.append(r)
return nouns
def lda_train(raw):
stop = set(stopwords.words('english'))
p_stemmer = PorterStemmer()
text_array = []
for i in range(len(raw)):
text = raw[i].lower()
text = text.replace('\r\n', ' ')
text = re.sub("[^a-z0-9]", " ", text)
# Tokenization segments a document into its atomic elements.
words = text.split()
# Stop words
# Certain parts of English speech, like (for, or) or the word the are meaningless to a topic model.
# These terms are called stop words and need to be removed from our token list.
words = [j for j in words if j not in stop]
tokenized = nltk.word_tokenize(text)
tagged_sent = pos_tag(words)
words = [word for word,pos in tagged_sent if pos == 'NN']
# Stemming words is another common NLP technique to reduce topically similar words to their root.
# stemming reduces those terms to stem. This is important for topic modeling, which would otherwise view those terms as separate entities and reduce their importance in the model.
#words = [p_stemmer.stem(s) for s in words]
text_array.append(words)
dictionary = corpora.Dictionary(text_array)
dictionary.save('dictionary.dic')
corpus = [dictionary.doc2bow(text) for text in text_array]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
ldamodel = models.ldamodel.LdaModel(corpus, num_topics=15, id2word=dictionary, passes=20)
filename = 'finalized_model_15.sav'
joblib.dump(ldamodel, filename)
print(ldamodel.print_topics(num_topics=15, num_words=6))
return ldamodel,dictionary
def filter_by_pos(sentence, pos):
"""Returns the sentence with only words whose part of speech is in
ACCEPTED_POS.
"""
words_with_pos = pos_tag(word_tokenize(sentence))
words_with_pos = filter(lambda word: word[1] in pos, words_with_pos)
return ' '.join(map(lambda word_with_pos: word_with_pos[0], words_with_pos))
def keep_nouns(tf):
n_tf = {}
for k in tf:
if pos_tag([k])[0][1].find('N') == 0:
n_tf[k] = tf[k]
return n_tf
def extract_tags(comment_file):
result = {}
fd = open(comment_file, "r")
for s in fd:
m = s.replace(",",".").replace("and", ".").replace("or",".").replace(":",".").split(".")
for f in m:
d = wordpunct_tokenize(f)
for index, t in enumerate(d):
pos_str = ""
if t in cellphone_attribute:
before = index-10
if before < 0:
before = 0
end = index + 10
if end > len(d)-1:
end = len(d)-1
pos_result = pos_tag(d[before:end])
for pos_index, pos_sent in enumerate(pos_result):
seg_for_word = ""
adjust_word = ""
if pos_sent[1].find("JJ") != -1:
seg_for_word = ' '.join(d[index:pos_index + before + 1])
adjust_word = pos_sent[0]
if pos_index+ before < index:
seg_for_word = ' '.join(d[pos_index+before:index+1])
add_into_dict(result, t, adjust_word, seg_for_word)
return result
def _get_nn(self, sentence):
'''get proper nouns'''
tagged_sent = pos_tag(sentence)
propernouns = [word for word,pos in tagged_sent if pos == 'NN']
regex = re.compile('[^a-zA-Z]')
propernouns = [regex.sub('', i) for i in propernouns]
return propernouns
def extractNounListFromTweetFile(filePath, fileName):
fileTarget = open(filePath + "\\" + fileName, 'r')
fileName = "processedTweets.txt"
processedTweetsFileTarget = open(filePath + "\\" + fileName , 'w')
allNouns = []
i = 0
for line in fileTarget:
lineContent = line.split("\t")
userId = lineContent[0]
tweet = lineContent[1]
#latitude = int(lineContent[2])
#longitude = int(lineContent[3].strip("\n"))
processedTweet, hashTags = preprocessTweet(tweet)
taggedTweet = pos_tag(processedTweet.split())
nounsInTweet = [word for word, pos in taggedTweet if pos == 'NNP' or pos == 'NN']
allNouns.extend(nounsInTweet)
processedTweetsFileTarget.write(str(i) + "\t" + userId + "\t" + processedTweet + "\t" + str(nounsInTweet) + "\t" + str(hashTags) + "\n")
i += 1
if i == 2000:
break
fileTarget.close()
processedTweetsFileTarget.close()
return allNouns
def extract(query):
sentence = query
tagged_sent = pos_tag(sentence.split())
propernouns = [word for word,pos in tagged_sent if pos == 'NN']
return propernouns
#extract("I want to buy a car and a dog and plane")
def extract_pos(tokens, simple=True):
"""
Simple parts of speech of speech are:
VERB - verbs (all tenses and modes)
NOUN - nouns (common and proper)
PRON - pronouns
ADJ - adjectives
ADV - adverbs
ADP - adpositions (prepositions and postpositions)
CONJ - conjunctions
DET - determiners
NUM - cardinal numbers
PRT - particles or other function words
X - other: foreign words, typos, abbreviations
. - punctuation
:param tokens:
:return:
"""
tokens_pos = pos_tag(tokens)
pos = [p for t, p in tokens_pos]
if simple:
# translate larger set of part of speech tags into small, simpler set
pos_dict = nltk.tagset_mapping('en-ptb', 'universal')
pos = [pos_dict[p] for p in pos]
return pos
def analiseSentimento(resposta):
texto = resposta['corpo']
frases = sentencesTokenizer.tokenize(texto)
palavras = []
for frase in frases:
palavras.extend(wordsTokenizer.tokenize(frase))
posTags = pos_tag(palavras)
positivo = 0
negativo = 0
for palavra, tag in posTags:
synsets = None
if tag.startswith('J'):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.ADJ)
elif tag.startswith('V'):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.VERB)
elif tag.startswith('N'):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.NOUN)
elif tag.startswith('R'):
synsets = sentiwordnet.senti_synsets(palavra, wordnet.ADV)
else:
synsets = sentiwordnet.senti_synsets(palavra, '')
if synsets != None:
synsets = list(synsets)
if len(synsets) > 0:
synset = synsets[0]
positivo = positivo + synset.pos_score()
negativo = negativo + synset.neg_score()
if positivo > negativo:
return (resposta, 'positivo')
elif negativo > positivo:
return (resposta, 'negativo')
else:
return (resposta, 'neutro')
def __init__(self, sentence):
assert type(sentence) == str
assert len(sentence) > 0
self.raw_text = sentence.strip()
self.lower = sentence.lower()
self.normalised = sentence.replace('"', '``').strip()
self.tokens, self.postags = zip(*pos_tag(word_tokenize(sentence)))
def returnPOSTaggedWords(text):
output={"CC":0,"CD":0,"DT":0,"EX":0,"FW":0,"IN":0,"JJ":0,"JJR":0,"JJS":0,"LS":0,"MD":0,"NN":0,"NNP":0,"NNPS":0,"NNS":0,"PDT":0,"POS":0,"PRP":0,"PRP$":0,"RB":0,"RBR":0,"RBS":0,"RP":0,"SYM":0,"TO":0,"UH":0,"VB":0,"VBD":0,"VBG":0,"VBN":0,"VBP":0,"VBZ":0,"WDT":0,"WP":0,"WP$":0,"WRB":0,"#":0,"$":0,"''":0,"(":0,")":0,",":0,".":0,":":0,"''":0,"-NONE-":0,"``":0}
tokens=wordpunct_tokenize(text)
tagged=pos_tag(tokens)
for word,pos in tagged:
output[pos]=output[pos]+1
return output
def find_catalog(product_name,catalog_of_products): temp_catalog=list(catalog_of_products) tagged_text=pos_tag(product_name.split()) output=nltk.ne_chunk(tagged_text) for subtree in output.subtrees(filter=lambda t: t.label() == 'PERSON'): for leave in subtree.leaves(): temp_catalog.append(leave[0]) return temp_catalog
def get_mistake_nouns(self):
mistakes_id = get_all_mistakes_id()
for id in mistakes_id:
phrase = get_mistake_noun(id)
tagged_sent = pos_tag(phrase.split())
nouns = [word for word, pos in tagged_sent if pos[0] == 'N']
for noun in nouns:
yield noun.strip('.')
# In[288]:
ps = PorterStemmer() #initialize Porter Stemmer object
ps_stems = []
for w in test_post_words:
ps_stems.append(ps.stem(w))
print(' '.join(ps_stems)) # add all the stemmed words to one string
# In[293]:
#parts of speech tagging
token_tag = pos_tag(test_post_words)
token_tag[:10]
# In[294]:
def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
elif treebank_tag.startswith('N'):
return wordnet.NOUN
elif treebank_tag.startswith('R'):
return wordnet.ADV
from nltk import word_tokenize
from nltk.tag import pos_tag
V = ['VB', 'VBZ', 'VBP', 'VBD', 'VBG']
N = ['NN', 'NNS', 'NNP', 'NNPS']
ADV = ['RB', 'RBR', 'RBS']
ADJ = ['JJ', 'JJR', 'JJS']
wLen = [] # number of words
vLen = [] # number of verbs
advLen = [] # number of adverbs
adjLen = [] # number of adjectives
vLen, nLen, advLen, adjLen, wLen = ([] for i in range(5))
for fileid in newcorpus.fileids():
tokens = word_tokenize(newcorpus.raw(fileid))
words = [t for t in tokens if t.isalpha()]
taggedW = pos_tag(words)
verbs, nouns, advs, adjs = ([] for i in range(4))
for (w,tag) in taggedW:
if tag in V: verbs.append(w)
elif tag in N: nouns.append(w)
elif tag in ADV: advs.append(w)
elif tag in ADJ: adjs.append(w)
wLen.append(len(words))
vLen.append(len(verbs))
nLen.append(len(nouns))
advLen.append(len(advs))
adjLen.append(len(adjs))
plotData0 = [(wLen, vLen), (wLen, nLen), (wLen, adjLen)]
yaxisLabels = ['V x 1000', 'N x 1000', 'ADJ x 1000']
# Named Entity Recognition
# Importing libraries
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk
# Importing the data
dataset = """Abraham Lincoln was an American statesman and lawyer
who served as the 16th President of the United States"""
# Tokenization and POS tagging
dataset_tag = pos_tag(word_tokenize(dataset))
# Apply NER
dataset_ner = ne_chunk(dataset_tag)
print(dataset_ner)
# Tree Diagram
dataset_ner.draw()
# app = FlaskAPI(__name__)
f=open('about2.txt','r',errors = 'ignore')
raw=f.read()
# raw=raw.lower()
# nltk.download('punkt') # first-time use only
# nltk.download('wordnet') # first-time use only
# nltk.download('averaged_perceptron_tagger') #once
sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences
word_tokens = nltk.word_tokenize(raw)# converts to list of words
propernouns = [word for word,pos in pos_tag(nltk.word_tokenize(raw)) if pos == 'NNP']
# print('propernouns')
# print(propernouns)
# print(word_tokens)
# new_word_tokens = []
# for w in word_tokens:
# if w in propernouns:
# new_word_tokens.append(w)
# else:
# new_word_tokens.append(w.lower())
# word_tokens = new_word_tokens
# print(word_tokens)
def _build_matrix(chunk=15):
# daily_updown = DailyStock.objects.all().order_by("diff_yesterday")
# daily_updown = daily_updown.values('year', 'month', 'date', 'diff_yesterday')
#
# minval = abs(min(map(itemgetter('diff_yesterday'), daily_updown)))
# maxval = abs(max(map(itemgetter('diff_yesterday'), daily_updown)))
# if minval > maxval:
# daily_updown = [dict(d, diff_yesterday=(
# d['diff_yesterday']) / minval) for d in daily_updown]
# else:
# daily_updown = [dict(d, diff_yesterday=(
# d['diff_yesterday']) / maxval) for d in daily_updown]
documents = []
up_days = list(DailyStock.objects.order_by('diff_yesterday')[:chunk])
down_days = list(DailyStock.objects.order_by('-diff_yesterday')[:chunk])
max_diff = max(up_days[0].diff_yesterday, abs(down_days[0].diff_yesterday))
daily_updown = up_days + down_days
for daily in daily_updown:
daily.diff_yesterday = daily.diff_yesterday / max_diff
text_dict = {}
days_text_list = []
for daily in daily_updown:
# dateconversion
print(daily)
LINK = 'https://news.google.com/rss/search?q=samsung+electronics+when:{}-{:02d}-{:02d}&hl=en-US&gl=US&ceid=US:en'.format(
daily.year,
daily.month,
daily.date,
)
print(LINK)
xmldoc = minidom.parse(urllib.request.urlopen(LINK, timeout=10))
itemlist = xmldoc.getElementsByTagName('item')
today_text = ''
# for newsitem in itemlist:
doccount = 0
for items in itemlist:
if doccount > 10:
break
print(items)
singlelink = items.getElementsByTagName(
'link')[0].firstChild.nodeValue
pubdate = items.getElementsByTagName(
'pubDate')[0].firstChild.nodeValue
date = date_parser.parse(pubdate).strftime("%Y%m%d")
try:
response = get(singlelink, timeout=10)
except Exception as e:
print('here')
pass
else:
try:
extractor = Goose()
article = extractor.extract(raw_html=response.content)
text_str = article.cleaned_text
today_text += text_str
doccount += 1
except TypeError:
print('this')
pass
days_text_list.append(today_text)
DailyDocument.objects.create(doc=today_text,
is_up=daily.diff_yesterday > 0)
scripts = days_text_list
lemmatizer = WordNetLemmatizer()
for sen in range(0, len(scripts)):
# Remove all the special characters
document = re.sub(r'\W', ' ', str(scripts[sen]))
# remove all single characters
document = re.sub(r'\s+[a-zA-Z]\s+', ' ', document)
# Remove single characters from the start
document = re.sub(r'\^[a-zA-Z]\s+', ' ', document)
# Substituting multiple spaces with single space
document = re.sub(r'\s+', ' ', document, flags=re.I)
# Converting to Lowercase
document = document.lower()
# Lemmatization
document = document.split()
doc = pos_tag(document)
final_doc = []
for i in range(len(doc)):
if doc[i][1] in ['NN', 'NNP', 'NNS', 'NNPS']:
document[i] = lemmatizer.lemmatize(document[i], 'n')
final_doc.append(document[i])
elif doc[i][1] in ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']:
document[i] = lemmatizer.lemmatize(document[i], 'v')
final_doc.append(document[i])
elif doc[i][1] in ['JJ', 'JJR', 'JJS']:
document[i] = lemmatizer.lemmatize(document[i], 'a')
final_doc.append(document[i])
elif doc[i][1] in ['RB', 'RBR', 'RBS', 'RP']:
document[i] = lemmatizer.lemmatize(document[i], 'r')
final_doc.append(document[i])
#else:
# final_doc.append(document[i])
pre_document = ' '.join(final_doc)
documents.append(pre_document)
vectorizer = TfidfVectorizer(stop_words='english',
token_pattern=r'(?u)\b[A-Za-z]+\b',
max_df=0.9)
bag_of_words = vectorizer.fit_transform(documents)
value_word = pd.DataFrame(bag_of_words.toarray()).mul(list(
map(lambda d: d.diff_yesterday, daily_updown)),
axis=0).sum(axis=0)
string_word = vectorizer.get_feature_names()
word_dict = {
string_word[i]: value_word[i]
for i in range(len(string_word))
}
with open('word_dict_pickle', 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
# print(node_ids)
edge_id = 0
for s,t,l in js:
try:
gjson.setdefault('edges',[]).append({'id': edge_id,
'from_id': node_ids[s],
'label': l,
'trg_id': node_ids[t],
'relxn': 'inferred'})
except Exception as e:
print(t,s,t)
print(str(e))
edge_id += 1
# print(
# json.dumps(gjson, indent=2, sort_keys=False)
# )
# https://stackoverflow.com/questions/17966554/in-python-nltk-i-am-trying-to-get-parts-of-speech-of-a-word-by-using-pos-tag-bu
#https://becominghuman.ai/natural-language-processing-in-python-3-using-nltk-fd0ff4a0da9b
for t in all_nodes:
gjson.setdefault('nodes', []).append({'id': node_ids[t],
'term': t,
'pos': pos_tag([t])[0][1] })
print(
json.dumps(gjson, indent=2, sort_keys=False)
)
da['targetDescription_subjectivity'] = data['targetDescription'].apply(lambda x : TextBlob(str(x)).sentiment.subjectivity)
da['targetParagraphs_polarity'] = data['targetParagraphs'].apply(lambda x : TextBlob(str(x)).sentiment.polarity)
da['targetParagraphs_subjectivity'] = data['targetParagraphs'].apply(lambda x : TextBlob(str(x)).sentiment.subjectivity)
da['targetTitle_polarity'] = data['targetTitle'].apply(lambda x : TextBlob(str(x)).sentiment.polarity)
da['targetTitle_subjectivity'] = data['targetTitle'].apply(lambda x : TextBlob(str(x)).sentiment.subjectivity)
# In[34]:
pos_di = {}
tagdict = load('help/tagsets/upenn_tagset.pickle')
for pos in list(tagdict.keys()):
pos_di[pos] = []
for snt in data['postText']:
di = Counter([j for i,j in pos_tag(word_tokenize(snt))])
for pos in list(tagdict.keys()):
pos_di[pos].append(di[pos])
da = pd.concat([da,pd.DataFrame(pos_di)], axis = 1)
#number of stop words
stp_wds = set(stopwords.words('english'))
da['postText_number_of_stop_words'] = data['postText'].apply(lambda x: len(stp_wds.intersection(word_tokenize(str(x)))))
#number of punctations
da['postText_num_of_unique_punctuations'] = data['postText'].apply(lambda x : len(set(x).intersection(set(string.punctuation))))
# In[35]:
import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import tree2conlltags
from pprint import pprint
# Read text file
text = open(
"Dataset/train.txt") # change the path of train.txt / valid.txt / test.txt
text = text.read()
# Convert text to word
word_token = word_tokenize(text)
# performe POS Tagging
word_pos = pos_tag(word_token)
# Define pattern for POS
pattern = 'NP: {<DT>?<JJ>*<NN>}'
#Tag BOI for POS
cp = nltk.RegexpParser(pattern)
cs = cp.parse(word_pos)
#BOI Tagging on POS Tagging
iob_tagged = tree2conlltags(cs)
print(iob_tagged)
NER_List = []
def adapted_lesk(word, sentence, context_window_size=3, pos=None):
"""Performs word sense disambiguation using the Adapted Lesk Algorithm,
due to Banerjee and Pedersen.
Arguments:
*word* (str) -- the target word to be disambiguated \n
*sentence* (str) -- the context in which the target word occurs \n
*context_window_size* (int) -- the number of words from the left and
right of the target word to be taken into analysis \n
*pos* (str) -- the part of speech of the target word
Returns:
Synset type -- the WordNet sense of the disambiguated word
"""
# Tokenize input sentence, remove punctuation and stopwords
sentence = utils.remove_stopwords(utils.remove_punctuation(w_tok(sentence)))
# Perform lemmatization on sentence
lemmatizer = WordNetLemmatizer()
tagged_sentence = pos_tag(sentence)
sentence = [lemmatizer.lemmatize(tup[0], utils.get_wordnet_pos(tup[1]))
for tup in tagged_sentence]
# Perform lemmatization on target word
if pos == None:
tagged_word = pos_tag([word])
word = lemmatizer.lemmatize(tagged_word[0][0],
utils.get_wordnet_pos(tagged_word[0][1]))
pos = utils.get_wordnet_pos(tagged_word[0][1])
else:
word = lemmatizer.lemmatize(word, pos)
# Extract the context window from the sentence
if word in sentence:
word_index = sentence.index(word)
if word_index - context_window_size < 0:
window_words = sentence[0 : word_index + context_window_size + 1]
else:
window_words = sentence[word_index - context_window_size :
word_index + context_window_size + 1]
# Take the Synsets of the target word
senses = wn.synsets(word)
best_sense = senses[0]
best_score = 0
for sense in senses:
if sense.pos() == pos:
# Only take the current sense into account if it is the correct pos
score = 0
for w in window_words:
if w != word:
w_senses = wn.synsets(w)
for w_sense in w_senses:
score += similarity(sense, w_sense, pos)
if score > best_score:
best_score = score
best_sense = sense
else: # If target word is not in context, after lemmatizing, return first wordnet sense
f = open('logs/guessed.txt', 'a')
line = "word: " + word + " in sentence: " + ' '.join(sentence)
f.write(line + '\n')
f.close()
return wn.synsets(word)[0]
return best_sense
""" Created on Tue Apr 16 10:33:57 2019 @author: [email protected] Information extraction https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da """ import nltk from nltk.tokenize import word_tokenize, sent_tokenize from nltk.tag import pos_tag ex = 'European authorities fined Google a record $5.1 billion on Wednesday for abusing its power in the mobile phone market and ordered the company to alter its practices' sent = word_tokenize(ex) sent = pos_tag(sent) #we get tuples of words with their respective pos tags. But the funny tihng with pos tags is that #they change for the same words depending on the occurance of the word in a sentence. #Chunking to extract ners "Lets create a chunk NP for extracting noun phrases" pattern = "NP:{<DT>?<JJ>*<NN>}" #lets test it cp = nltk.RegexpParser(pattern) cs = cp.parse(sent) print(cs)
def extract_NNPs(text):
tagged = pos_tag(text.split())
NNPs = [word for word, pos in tagged if pos == 'NNP']
return NNPs
def factSequenceAnalysis(path):
tree = ET.parse(path)
root = tree.getroot()
root = tree.getroot()
yRange = 0
#fil = open("output.txt",'w')
result = []
f = 0
old_rev = []
count_rev = 0
total_facts = []
#fil = open("output.txt","w");
for rev in root.find(
'{http://www.mediawiki.org/xml/export-0.10/}page').findall(
'{http://www.mediawiki.org/xml/export-0.10/}revision'):
count_rev += 1
'''
if(count_rev==3):
break
'''
text = rev.find('{http://www.mediawiki.org/xml/export-0.10/}text').text
if (not text):
total_facts.append(0)
continue
tags = ["NNP", "NNPS"]
tagged_sent = pos_tag(word_tokenize(text))
#fil.write("TimeStamp:"+rev.find("{http://www.mediawiki.org/xml/export-0.10/}timestamp").text+"\n");
current_rev = []
#fil.write("TimeStamp:"+rev.find("{http://www.mediawiki.org/xml/export-0.10/}timestamp").text+"\n");
if (f == 0):
yRev = []
count_y = 1
for tagged_word in tagged_sent:
if hasNumbers(tagged_word[0]) == False and hasPunctuations(
tagged_word[0]) == False and len(
tagged_word[0]
) > 1: #to remove words like ",","132" etc.
if (tagged_word[1] in tags):
#fil.write(tagged_word[0]+" , ")
old_rev.append(str(tagged_word[0]))
yRev.append(count_y)
count_y += 1
#fil.write("\n=====================================================================\n")
#print(old_rev)
total_facts.append(len(old_rev))
if (len(old_rev) > yRange):
yRange = len(old_rev)
result.append(yRev)
f = 1
else:
for tagged_word in tagged_sent:
if hasNumbers(tagged_word[0]) == False and hasPunctuations(
tagged_word[0]) == False and len(
tagged_word[0]
) > 1: #to remove words like ",","132" etc.
if (tagged_word[1] in tags):
#fil.write(tagged_word[0]+" , ")
current_rev.append(str(tagged_word[0]))
new_list = find_diff(old_rev, current_rev)
total_facts.append(len(current_rev))
#print(current_rev)
yRev = new_list[0]
if (len(current_rev) > yRange):
yRange = len(current_rev)
result.append(yRev)
old_rev = current_rev
#print("one revision completed!!")
#fil.write("\n=====================================================================\n")
result.append(total_facts)
result.append(count_rev)
return result
review += arr.strip() #adds it to var review
arr = fileReview.read()
fileReview.close()
print "Sentence tokenization..."
review_dict = sent_tokenize(review) #tokenizes sentences
arr_pos = []
removed = []
print "POS tagging for words..."
#arr_sent = pos_tag(review_dict) #tagging words for semantic
#annotation
for sent in review_dict: #adding individual sentences after tagging
arr_pos.extend([pos_tag(sent.split())].__iter__())
################################################################################
################################################################################
print "Loading Parser..."
#t = npc.parse(tmp_arr_pos[0])
print "Finished loading..."
#print len(t)
#t.draw()
#help(t)
sentCount = 1
sentScore = [] #tuple with (Subj-Obj , Verb-P , )
totalS = []
swlist = stopwords.words('english')
stemmer = PorterStemmer()
pos_corpus = []
neg_corpus = []
for f in pos:
# match any special character and remove it (other than _)
processed_content = re.sub(r'\W+', ' ', f.lower())
# text into tokens
words = word_tokenize(processed_content)
# Attaching part of speech to each word
pos_words = pos_tag(words) #returns a list
clean_words = []
for w in pos_words:
if w[0] in swlist or len(w[0]) <= 3 or w[1] not in (
'JJ', 'JJR', 'JJS', 'NN', 'NNS', 'ADJ', 'ADV', 'VBN', 'VBG'):
continue
clean_words.append(stemmer.stem(w[0]))
pos_content = ' '.join(clean_words)
pos_corpus.append(pos_content)
for f in neg:
processed_content = re.sub(r'\W+', ' ', f.lower())
words = word_tokenize(processed_content)
neg_words = pos_tag(words)
import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk
sentence = 'European authorities fined Google a record $5.1 billion on Wednesday for abusing its power in the mobile phone market and ordered the company to alter its practices'
ne_tree = ne_chunk(pos_tag(word_tokenize(sentence)))
print(ne_tree)
ex = 'European authorities fined Google a record $5.1 billion on Wednesday for abusing its power in the mobile phone market and ordered the company to alter its practices'
def preprocess(sent):
sent = nltk.word_tokenize(sent)
sent = nltk.pos_tag(sent)
return sent
sent = preprocess(ex)
sent
pattern = 'NP: {<DT>?<JJ>*<NN>}'
import shanepy
import shanepy as spy
from shanepy import *
cp = nltk.RegexpParser(pattern)
cs = cp.parse(sent)
#Stemming
from nltk.stem import PorterStemmer, LancasterStemmer, SnowballStemmer
print("\n\n)")
print("STEMMING")
for WORD in WORDS:
stemmer = PorterStemmer()
print(WORD, stemmer.stem(WORD))
#POS
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
print("\n\n")
print("POS-TAG")
for WORD in WORDS:
print(WORD, pos_tag(WORD))
#LEMMATIZATION
from nltk.stem import WordNetLemmatizer
print("\n\n")
print("LEMMATIZATION")
lemmatizer = WordNetLemmatizer()
for WORD in WORDS:
print(WORD, lemmatizer.lemmatize(WORD))
#TRIGRAM
from nltk import word_tokenize
from nltk.util import ngrams
print("\n\n")
print("TRIGRAM")
input_list = []