本文整理汇总了Python中pymystem3.Mystem.lemmatize方法的典型用法代码示例。如果您正苦于以下问题：Python Mystem.lemmatize方法的具体用法？Python Mystem.lemmatize怎么用？Python Mystem.lemmatize使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pymystem3.Mystem的用法示例。
在下文中一共展示了Mystem.lemmatize方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: mystem_using_with_considering_of_multiple_letters
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def mystem_using_with_considering_of_multiple_letters(input_directory, output_directory):
input_files = filter(lambda x: not x.endswith('~'), os.listdir(input_directory))
output_data = {}
m = Mystem()
#иду по документам
for input_file in input_files:
with open(input_directory + '/' + input_file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != '', re.split(''' |\.|,|:|\?|"|\n|<|>|\*|!|@|_ +''', data['text']))
my_list_of_terms = []
for term in list_of_terms:
if term == m.lemmatize(term)[0]:
my_term = term
term = u''
prev_letter = my_term[0]
term += my_term[0]
for i in range(1, len(my_term)):
if my_term[i] != prev_letter:
term += my_term[i]
prev_letter = my_term[i]
my_list_of_terms.append(term)
else:
my_list_of_terms.append(term)
list_of_terms = my_list_of_terms
text = ' '.join(['%s' % term for term in list_of_terms])
list_of_terms = filter(lambda x: x not in ('', ' ', '\n'), m.lemmatize(text))
text_of_output = ' '.join(['%s' % term for term in list_of_terms])
output_data[input_file] = {}
output_data[input_file]['id'] = data['id']
output_data[input_file]['positive'] = data['positive']
output_data[input_file]['sarcasm'] = data['sarcasm']
output_data[input_file]['text'] = text_of_output
with open(output_directory + '/' + input_file, 'w') as output_file:
json.dump(output_data[input_file], output_file)示例2: extract
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def extract(self):
try:
#вычисляем, сколько в директории лежит файлов
input_files = filter(lambda x: not x.endswith('~'), os.listdir(self.input_directory))
output_data = {}
list_of_all_terms = {}
m = Mystem()
#иду по документам
for file in input_files:
with open(self.input_directory + '/' + file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != "", re.split(""" |\.|,|:|\?|"|<|>|\*|!|@|_ +""", data['text']))
text = " ".join(["%s" % term for term in list_of_terms])
list_of_terms = filter(lambda x: x != " ", m.lemmatize(text))
count_of_rows = 0
for i in range(0, len(list_of_terms)):
if list_of_terms[i] == '\n' or list_of_terms[i] == ' \n':
count_of_rows += 1
if list_of_terms[i] == ' \n':
list_of_terms[i] = '\n'
if count_of_rows < self.threshold_of_rows_count:
first_list_of_terms = list_of_terms
list_of_terms = []
for i in range(0, len(first_list_of_terms)):
if first_list_of_terms[i] != '\n':
list_of_terms.append(first_list_of_terms[i])
output_data[file] = {}
output_data[file]['id'] = data['id']
output_data[file]['positive'] = data['positive']
output_data[file]['sarcasm'] = data['sarcasm']
output_data[file]['terms'] = {}
#убираю повторяющиеся слова
for term in list_of_terms:
if term not in output_data[file]['terms']:
output_data[file]['terms'][term] = 1
else:
output_data[file]['terms'][term] += 1
for term in output_data[file]['terms'].keys():
if term not in list_of_all_terms:
list_of_all_terms[term] = 1
else:
list_of_all_terms[term] += 1
#подсчёт tf
count_of_terms = output_data[file]['terms'][term]
output_data[file]['terms'][term] = {'tf': float(count_of_terms)/len(list_of_terms), 'idf': 0,
'count': count_of_terms}
for file in input_files:
#подсчёт idf
for term in output_data[file]['terms'].keys():
output_data[file]['terms'][term]['idf'] = math.log(float(len(input_files))/list_of_all_terms[term])
#запись результата
with open(self.output_directory + '/' + file + '_tf-idf', 'w') as output_file:
json.dump(output_data[file], output_file)
except Exception:
return False
else:
return True示例3: extract
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def extract(self):
try:
#вычисляем, сколько в директории лежит файлов
input_files = filter(lambda x: not x.endswith('~'), os.listdir(self.input_directory))
output_data = {}
list_of_all_terms = {}
m = Mystem()
#иду по документам
for file in input_files:
with open(self.input_directory + '/' + file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != "", re.split(""" |\.|,|:|\?|"|\n|<|>|\*|!|@|_ +""", data['text']))
text = " ".join(["%s" % term for term in list_of_terms])
list_of_terms = filter(lambda x: x not in (" ", "\n"), m.lemmatize(text))
my_list = list_of_terms
list_of_terms = []
for term in my_list:
if m.analyze(term)[0].get(u'analysis'):
if not m.analyze(term)[0][u'analysis'][0][u'gr'].startswith(self.service_parts_of_speech) and len(term) > 1:
list_of_terms.append(term)
if term == u'не':
list_of_terms.append(term)
else:
list_of_terms.append(term)
output_data[file] = {}
output_data[file]['id'] = data['id']
output_data[file]['positive'] = data['positive']
output_data[file]['sarcasm'] = data['sarcasm']
output_data[file]['terms'] = {}
#убираю повторяющиеся слова
for term in list_of_terms:
if term not in output_data[file]['terms']:
output_data[file]['terms'][term] = 1
else:
output_data[file]['terms'][term] += 1
for term in output_data[file]['terms'].keys():
if term not in list_of_all_terms:
list_of_all_terms[term] = 1
else:
list_of_all_terms[term] += 1
#подсчёт tf
count_of_terms = output_data[file]['terms'][term]
output_data[file]['terms'][term] = {'tf': float(count_of_terms)/len(list_of_terms), 'idf': 0,
'count': count_of_terms}
for file in input_files:
#подсчёт idf
for term in output_data[file]['terms'].keys():
output_data[file]['terms'][term]['idf'] = math.log(float(len(input_files))/list_of_all_terms[term])
#запись результата
with open(self.output_directory + '/' + file + '_tf-idf', 'w') as output_file:
json.dump(output_data[file], output_file)
except Exception:
return False
else:
return True开发者ID:pombredanne，项目名称:senty，代码行数:58，代码来源:standard_extractor_with_mystem_without_service_parts_of_speech.py
示例4: Runner
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
class Runner(object):
def __init__(self, input_text):
self.lemmatize = None
while True:
response = raw_input("Do you want to lemmatize text first? (yes/no)\n").lower()
if response == "yes":
print "You should wait for a while"
self.lemmatize = True
self.stemmer = Mystem()
break
elif response == "no":
self.lemmatize = False
break
self.word_lists = list()
with open(input_text, "r") as f:
for line in f:
line += "."
if self.lemmatize:
lexemes = self.stemmer.lemmatize(line)
word_list = list() # список слов, неразделенных знаками пунктуации
for lexeme in lexemes:
lexeme = lexeme.strip()
if lexeme:
if lexeme.translate(None, '.,?!:;()"\' -\t\n'): # проверка, что лексема не является знаком пунктуации
lexeme = lexeme.decode("utf-8")
if is_cyrillic(lexeme):
word_list.append(lexeme)
else: # иначе, добавить биграмы из списка и завести новый пустой список
self.word_lists.append(word_list)
word_list = list()
else:
line = line.replace(".", " . ").replace(",", " , ").replace(":", " : ").replace(";", " ; ")\
.replace("?", " ? ").replace("!", " ! ").replace("(", " ( ").replace(")", " ) ")\
.replace("--", " -- ").replace(".", " . ")
word_list = list()
for lexeme in line.split():
# проверка, что лексема не является знаком пунктуации
lexeme = lexeme.translate(None, '.,?!:;()"\'').replace("--", "").decode("utf-8").strip().lower()
if lexeme:
if is_cyrillic(lexeme):
word_list.append(lexeme)
else:
if word_list:
self.word_lists.append(word_list)
word_list = list()
train, test = self.split()
self.lid = Lid(train, test)
self.lid.run()
def split(self):
n = len(self.word_lists)
train = self.word_lists[:n*9/10]
test = self.word_lists[n*9/10:]
return train, test示例5: Index
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
class Index(object):
def __init__(self, input_file):
self.stemmer = Mystem()
self.documents = dict()
self.tokens = list()
self.terms = dict()
self.index = list()
# reading documents, making tokenization
with open(input_file, "r") as f:
for i, line in enumerate(f, start=1):
self.documents[i] = line.decode("utf-8")
for word in self.stemmer.lemmatize(line):
token = word.translate(None, '.,?!:;()"\'-').decode("utf-8").strip()
if token:
self.tokens.append((token, i))
# sorting by tokens first, then by frequency
self.tokens.sort(key=lambda tup: (tup[0], tup[1]))
# terminization and building index
current_term = self.tokens[0][0]
current_doc_id = self.tokens[0][1]
doc_ids = [current_doc_id]
for token, doc_id in self.tokens:
term = token.lower()
if term == current_term:
if doc_id != current_doc_id:
doc_ids.append(doc_id)
current_doc_id = doc_id
else:
self.terms[current_term] = (len(doc_ids), doc_ids)
self.index.append((current_term, len(doc_ids), doc_ids))
current_term = term
current_doc_id = doc_id
doc_ids = [doc_id]
self.terms[current_term] = (len(doc_ids), doc_ids)
self.index.append((current_term, len(doc_ids), doc_ids))
def print_to_file(self):
with open("result.txt", "w") as f:
for term, count, doc_ids in self.index:
f.write("{},\t{},\t{}\n".format(term.encode("utf-8"), count, doc_ids))
def print_statistics(self):
terms_num = len(self.terms)
terms_len = 0.
for term in self.terms:
terms_len += len(term)
print "***********************"
print "Number of terms = {}".format(terms_num)
print "Average term length = {}".format(terms_len / terms_num)
print "***********************"示例6: extract
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def extract(self):
try:
#вычисляем, сколько в директории лежит файлов
input_files = filter(lambda x: not x.endswith('~'), os.listdir(self.input_directory))
output_data = {}
list_of_all_n_grams = {}
m = Mystem()
#иду по документам
for file in input_files:
with open(self.input_directory + '/' + file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != "", re.split(""" |\.|,|:|\?|"|\n|<|>|\*|!|@|_ +""", data['text']))
text = " ".join(["%s" % term for term in list_of_terms])
list_of_terms = filter(lambda x: x not in (" ", "\n"), m.lemmatize(text))
list_of_n_grams_tuples = {}
for j in range(0, self.n):
list_of_n_grams_tuples[j] = zip(*[list_of_terms[i:] for i in range(j + 1)])
list_of_n_grams_strings = []
for j in range(0, self.n):
for gram_tuple in list_of_n_grams_tuples[j]:
string_of_n_gram = " ".join(["%s" % term for term in gram_tuple])
list_of_n_grams_strings.append(string_of_n_gram)
output_data[file] = {}
output_data[file]['id'] = data['id']
output_data[file]['positive'] = data['positive']
output_data[file]['sarcasm'] = data['sarcasm']
output_data[file]['terms'] = {}
#убираю повторяющиеся слова
for gram in list_of_n_grams_strings:
if gram not in output_data[file]['terms']:
output_data[file]['terms'][gram] = 1
else:
output_data[file]['terms'][gram] += 1
for gram in output_data[file]['terms'].keys():
if gram not in list_of_all_n_grams:
list_of_all_n_grams[gram] = 1
else:
list_of_all_n_grams[gram] += 1
#подсчёт tf
count_of_n_grams = output_data[file]['terms'][gram]
output_data[file]['terms'][gram] = {'tf': float(count_of_n_grams)/len(list_of_n_grams_strings), 'idf': 0,
'count': float(count_of_n_grams)}
for file in input_files:
#подсчёт idf
for gram in output_data[file]['terms'].keys():
output_data[file]['terms'][gram]['idf'] = math.log(float(len(input_files))/list_of_all_n_grams[gram])
#запись результата
with open(self.output_directory + '/' + file + '_tf-idf', 'w') as output_file:
json.dump(output_data[file], output_file)
except Exception:
return False
else:
return True示例7: mystem_using
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def mystem_using(input_directory, output_directory):
input_files = filter(lambda x: not x.endswith('~'), os.listdir(input_directory))
output_data = {}
m = Mystem()
for input_file in input_files:
with open(input_directory + '/' + input_file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != '', re.split(''' |\.|,|:|\?|"|\n|<|>|\*|!|@|_ +''', data['text']))
text = " ".join(["%s" % term for term in list_of_terms])
list_of_terms = filter(lambda x: x not in ('', ' ', '\n'), m.lemmatize(text))
text_of_output = ' '.join(['%s' % term for term in list_of_terms])
output_data[input_file] = {}
output_data[input_file]['id'] = data['id']
output_data[input_file]['positive'] = data['positive']
output_data[input_file]['sarcasm'] = data['sarcasm']
output_data[input_file]['text'] = text_of_output
with open(output_directory + '/' + input_file, 'w') as output_file:
json.dump(output_data[input_file], output_file)示例8: search
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def search():
cn = None
file = codecs.open('static/articles.xml', 'r', 'utf-8')
rfile = file.read()
tree = lxml.etree.fromstring(rfile)
res = tree.xpath('entry')
categ = {
'cat': 'Категория', 'wgroup': 'Группа слов с близким значением', 'comm': 'Комментарии',
'stdiff': 'Стилистические различия', 'overlap': 'Совпадающая часть значения',
'dom': 'Доминанта группы', 'diffmark': 'Различительные признаки, релевантные для данной группы',
'diff': 'Смысловые различия', 'rare': 'Редкие слова, примыкающие к группе',
'anmean': 'Другие значения слов, входящих в группу', 'comb': 'Сочетаемость', 'reg': 'Региональные варианты',
'adict': 'Данные академических словарей', 'doc': 'Нормативные документы',
'etim': 'Этимология', 'ill': 'Иллюстрации'
}
file.close()
ms = Mystem()
wordsearch = ms.lemmatize(request.form['search'].lower())[0]
for i in res:
if wordsearch == '':
cn = 'Пустой запрос'
elif i.text.lower().startswith(wordsearch):
arr = []
for j in i.iter():
for k in dict.keys(categ):
if j.tag == k:
if j.text != 'null':
arr.append('<font size="4"><b>' + str(categ[j.tag]) + '</b></font><br>' + str(j.text))
text = '<br><br>'.join([j for j in arr[1:]])
text = re.sub('\*', '<b>', text)
text = re.sub('\#', '</b>', text)
text = re.sub('\$', '<i>', text)
text = re.sub('\%', '</i>', text)
text = re.sub('\@', '<font color="#696969">', text)
text = re.sub('\+', '</font>', text)
cn = '<strong><big>' + i.text + '</big></strong><br><br>' + re.sub('\n', '<br>', text)
break
else:
cn = 'По Вашему запросу ничего не найдено. <br>' \
'Попробуйте использовать "Поиск по тегу" или измените запрос.'
return render_template('search.html', cn=Markup(cn))示例9: Mystem
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
from pymystem3 import Mystem
m = Mystem()
t = 'Чайника, сегодня не было'
lemma = m.lemmatize(t)
def lemmas(text):
punc = list('.?!-;:",')
text = [i for i in text if i not in punc]
text = ''.join(text)
text = m.lemmatize(text)
textn = ''
for w in text:
if w is not ' ' or '\n':
textn += w
return textn
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
import os
s_w = stopwords.words('russian')
sw = [i for i in s_w]
v = TfidfVectorizer(stop_words=sw) # убираем стоп-слова
#v = TfidfVectorizer() # не убираем стоп-слова
totalCorpus = []
suspenseCorpus = ''示例10: lemma
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def lemma(text):
m = Mystem()
lemmas = m.lemmatize(text)
titleStemmed = ''.join(lemmas)
return titleStemmed示例11: open
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
with open("../data/" + PREFIX + "norm_sentences.txt", "w") as writer:
count = 0
raw = []
normalized = []
for line in open("../data/" + PREFIX + "parsed.txt"):
if count % 1000 == 0:
print count
line = re.sub("[\W\d]+", " ", line.strip().decode("utf-8").strip(), 0, re.UNICODE)
line = re.sub("\s+", " ", line.strip(), 0, re.UNICODE).lower()
raw.extend(line.split(" "))
writer.write("* " + line.encode("utf-8") + " **;")
# print line, '->',
line = " ".join(normalizer.lemmatize(line))
line = re.sub("\s+", " ", line, 0, re.UNICODE)
lemmatized = filter(lambda x: len(x.strip()) > 0, normalizer.lemmatize(line))
normalized.extend(lemmatized)
# print line
writer.write("* " + " ".join(lemmatized).encode("utf-8") + " **\n")
count += 1
# print 'saving raw'
#
# with open("../data/raw_terms.txt", "w") as f:
# for term in set(raw):
# f.write(term.encode("utf-8") + "\n")
#
# print 'saving norm'示例12: poehali
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
#.........这里部分代码省略.........
read_and_clean_xml = openindosug_xml.read()
xml_data = amixml(read_and_clean_xml)
#print(xml_data[2])
openindosug_xml.close()
'''
Созидание директории для plain текста
'''
create_folder(path, year, transpose_month(month), "plain")
forplain = path+"plain/"+year+"/"+transpose_month(month)+"/"+dest_html
forplain_dir = path+"plain/"+year+"/"+transpose_month(month)+"/"
shutil.copy(path+"html/"+year+"/"+transpose_month(month)+"/"+dest_html, forplain)
print("FILE "+str(i)+" HB COPIED TO PLAIN")
openindosug = open(forplain, "r")
dates = re.sub("\">", "", dates)
'''
wri = лист для генерации ИНФО о статьи
'''
wri = ["briansk.ru", str(xml_data[1]), toddmmyyy(dates), "", row['url']]
page2_txt = open(str(forplain_dir)+str(plain), 'w')
for datline in openindosug:
page2_txt.write(str(make_it_clean(datline)))
page2_txt.close()
print("PLAIN FOR "+str(i)+" HB CREATED")
'''
Окончательная очистка plain файла; оставляем только текст статьи или текст + ИНФО
'''
provide_clean_file(forplain_dir+str(plain),forplain_dir+str(plain_new), wri, "extra")
provide_clean_file(forplain_dir+str(plain),forplain_dir+str(plain_stem), wri, "mystem")
os.remove(forplain_dir+str(plain))
os.remove(forplain)
openindosug.close()
'''
xml_data[0] -- content
xml_data[1] -- headerTag
xml_data[2] -- content date
'''
'''
Генерация XML
'''
pageEtree = etree.Element('html')
doc = etree.ElementTree(pageEtree)
infoTag = etree.SubElement(pageEtree, "body")
dateTag = etree.SubElement(infoTag, "h1")
dateTag.text = str(xml_data[2])
headerTag = etree.SubElement(infoTag, "h2")
headerTag.text = str(xml_data[1])
mainTag = etree.SubElement(infoTag, "h3")
contentTag = etree.SubElement(infoTag, "h4")
contentTag.text = str(xml_data[0])
outFile = open(str(forxml_dir)+str(i)+".xml", 'wb')
doc.write(outFile, xml_declaration=True, encoding='utf-16')
outFile.close()
print("FILE "+str(i)+" HB CODED TO XML")
writer.writerow([str(path+"html/"+year+"/"+transpose_month(month)+"/"+dest_html) , "briansk.ru" , "" , "" , str(xml_data[1]) , toddmmyyy(dates), 'публицистика' , "" , "" , "категория" , "" , "нейтральный" , "н-возраст" , "н-уровень" , "городская" , str(row['url']) , "брянск.ru" , "" , str(year) , "газета" , "Россия" , "БРЯНСК" , "ru"])
os.remove(forxml)
input_plain = forplain_dir + plain_stem
output_plain = forplain_dir + output_plain_stem
'''
pystem
mystem
'''
with open(input_plain) as file:
text = file.read()
lemmas = m.lemmatize(text)
with open(input_plain, 'w') as file:
file.write(''.join(lemmas))
os.system(r'/home/haniani/Загрузки/mystem -icd '+ input_plain + ' ' + output_plain)
os.system(r'/home/haniani/Загрузки/mystem -icd --format xml '+ input_plain +' '+ xml_stem)
print("MYSTEM'ed "+str(i))
break
i += 1
print("PASSED ; NEXT: "+str(i)+"\n")
csv_file.close()
for file in glob.glob(path+"*.html"):
os.remove(file)示例13: open
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
with open(file_in) as parsed_in, \
open("..\\data\\stemmed\\" + name + "_mystem.tsv", "wb") as mystem_out:
# open("..\\data\\stemmed\\" + name + "_porter.tsv", "wb") as porter_out, \
parsed_in = csv.reader(parsed_in, delimiter='\t')
mystem_out = csv.writer(mystem_out, delimiter='\t') #, quoting=csv.QUOTE_NONE
mystem = Mystem()
prep_counter = 0
for row in parsed_in:
exclude = ['\'', '\"', '.', ',', '!', '?', u'«', u'»']
s = ''.join(ch for ch in row[1].decode("utf-8") if ch not in exclude)
stemmed_tokens = m.lemmatize(s)
stemmed_tokens = [token if emoticon_re.search(token) else token.lower() for token in stemmed_tokens]
# punctuation = list(string.punctuation.decode("utf-8"))
# stop = punctuation
# stop = ['!', '"', '$', '%', '&', '\'', '(', ')', '*', '+', ',', '-', '.', '/',
# ':', ';', '<', '=', '>', '?', '[', '\\', ']', '^', '_', '`', '{', '|', '}', '~'] #'@',
stop = ['rt', 'via', '...', "…".decode("utf-8")]
stemmed_tokens = [token if token not in stop else '' for token in stemmed_tokens]
stemmed_str = "".join([token for token in stemmed_tokens])
mystem_out.writerow([row[0], stemmed_str.encode("utf-8").replace('\n', ' ')])
# Print a status message every 1000th review
if prep_counter % 100. == 0.:
print "Lemmatize %d strings" % (prep_counter)示例14: extract
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
def extract(self):
try:
# вычисляем, сколько в директории лежит файлов
input_files = filter(lambda x: not x.endswith("~"), os.listdir(self.input_directory))
output_data = {}
list_of_all_terms = {}
m = Mystem()
# иду по документам
for file in input_files:
with open(self.input_directory + "/" + file) as data_file:
data = json.load(data_file)
list_of_terms = filter(lambda x: x != "", re.split(""" |\.|,|:|\?|"|\n|<|>|\*|!|@|_ +""", data["text"]))
text = " ".join(["%s" % term for term in list_of_terms])
list_of_terms = filter(lambda x: x not in (" ", "\n"), m.lemmatize(text))
my_list_of_terms = []
for term in list_of_terms:
my_term = term
term = u""
prev_letter = my_term[0]
term += my_term[0]
for i in range(1, len(my_term)):
if my_term[i] != prev_letter:
term += my_term[i]
prev_letter = my_term[i]
my_list_of_terms.append(term)
list_of_terms = my_list_of_terms
output_data[file] = {}
output_data[file]["id"] = data["id"]
output_data[file]["positive"] = data["positive"]
output_data[file]["sarcasm"] = data["sarcasm"]
output_data[file]["terms"] = {}
# убираю повторяющиеся слова
for term in list_of_terms:
if term not in output_data[file]["terms"]:
output_data[file]["terms"][term] = 1
else:
output_data[file]["terms"][term] += 1
for term in output_data[file]["terms"].keys():
if term not in list_of_all_terms:
list_of_all_terms[term] = 1
else:
list_of_all_terms[term] += 1
# подсчёт tf
count_of_terms = output_data[file]["terms"][term]
output_data[file]["terms"][term] = {
"tf": float(count_of_terms) / len(list_of_terms),
"idf": 0,
"count": count_of_terms,
}
for file in input_files:
# подсчёт idf
for term in output_data[file]["terms"].keys():
output_data[file]["terms"][term]["idf"] = math.log(
float(len(input_files)) / list_of_all_terms[term]
)
# запись результата
with open(self.output_directory + "/" + file + "_tf-idf", "w") as output_file:
json.dump(output_data[file], output_file)
except Exception:
return False
else:
return True开发者ID:pombredanne，项目名称:senty，代码行数:65，代码来源:standard_extractor_with_mystem_and_considering_multiple_letters.py
示例15: Mystem
# 需要导入模块: from pymystem3 import Mystem [as 别名]
# 或者: from pymystem3.Mystem import lemmatize [as 别名]
# Using pymystem3 lemmatize texts
import sys
from pymystem3 import Mystem
text = sys.argv[1]
m = Mystem()
lemmas = m.lemmatize(text)
print(''.join(lemmas))