def load_token_list(term_file):
'''
load some stopword list from the corpus
'''
__location__ = '../corpora/'
tokens = WordListCorpusReader(__location__, term_file)
return [w.replace('+', '\+') for w in tokens.words()]
def load_token_list(term_file):
'''
load some stopword list from the corpus
'''
__location__ = os.path.join(os.path.abspath(os.path.dirname(__file__)),
_corpus_root)
tokens = WordListCorpusReader(__location__, term_file)
return [w.replace('+', '\+') for w in tokens.words()]
def load_token_list(term_file):
'''
load some stopword list from the corpus
'''
__location__ = os.path.join(
os.path.abspath(os.path.dirname(__file__)), _corpus_root)
tokens = WordListCorpusReader(__location__, term_file)
return [w.replace('+', '\+') for w in tokens.words()]
def extract_mimetypes(text, do_replace=True):
'''
pull a list of mimetypes from some text feature
return a list of mimetypes in the text block and
the text, without mimetypes or unmodified
'''
mimetypes = WordListCorpusReader(_corpus_root, 'mimetypes.txt')
found_mimetypes = [w for w in mimetypes.words() if w in text]
if do_replace:
text = remove_tokens('mimetypes.txt', text)
return found_mimetypes, text
def __init_corpora(self):
self.negation_words = WordListCorpusReader('../data/corpora/',
'negation_words')
self.sent_ends = WordListCorpusReader('../data/corpora', 'sent_ends')
self.negative_sentiments = WordListCorpusReader(
'../data/corpora/sentiment-lexicon', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader(
'../data/corpora/sentiment-lexicon', 'positive-words.txt')
def extract_mimetypes(text, do_replace=True):
'''
pull a list of mimetypes from some text feature
return a list of mimetypes in the text block and
the text, without mimetypes or unmodified
'''
mimetypes = WordListCorpusReader(_corpus_root, 'mimetypes.txt')
found_mimetypes = [w for w in mimetypes.words() if w in text]
if do_replace:
text = remove_tokens('mimetypes.txt', text)
return found_mimetypes, text
def __init__(self, language, sw_files=[], load_default=True):
self.language = language
self.stopwords = []
if load_default:
wlcr = WordListCorpusReader(data.GzipFileSystemPathPointer(DEFAULT_SW_FILE), [language], encoding="utf-8")
self.stopwords = wlcr.words(language)
logging.info("Loaded default stopwords from file %s" % DEFAULT_SW_FILE)
path = BASE_SW_PATH + language
for sw_file in sw_files:
wlcr = WordListCorpusReader(data.FileSystemPathPointer(path), sw_file, encoding="utf-8")
self.stopwords += wlcr.words(sw_file)
logging.info("Loaded stopwords from file '%s'" % sw_file)
@author: ake
@software: PyCharm Community Edition
@time: 2016/4/28 9:32
"""
import re
from gensim import corpora, models, similarities
import xml.etree.ElementTree as Et
from GetData.preprocess import getdata, ltp, ltp_pos
from nltk.corpus import WordListCorpusReader
import jieba
import jieba.posseg as pseg
import logging
# 添加停用词
STOP_PATH = r'D:\MyProject\pythonProjects\TopicMine\LDA_T\data\\'
stopwords = set(WordListCorpusReader(STOP_PATH, 'stopwords.txt').words())
#
def parse_lda_xml(file):
with open(file, 'r', encoding='utf-8') as f:
xml_raw = f.read().strip().split('\n\n') # xml文本，可能包含多个xml，用双换行进行切分
f.close()
docs = [] # 存储结果
for doc in xml_raw:
xml = Et.fromstring(doc)
doc_words = []
for sentence in xml.findall('./doc/para/sent'): # 循环读取句子
word_list = [words for words in sentence] # 循环读取word列表
wordsall = [] # 存储句子的匹配结果
for word in word_list: # 循环解析每个word要素
def read_stopwords(path):
'''使用nltk读停用词表
'''
root,fileid=os.path.split(path)
stopwords=WordListCorpusReader(root,[fileid])
return stopwords.words(fileid)
class OpinionSentenceFinder:
def __init__(self, features, feature_sentences):
self.feature_sentences = feature_sentences
self.opinion_sentences = []
self.features = features
self.__init_corpora()
for sent_index in xrange(len(self.feature_sentences)):
sent = self.feature_sentences[sent_index]
self.feature_sentences[sent_index]['opinion_sent'] = []
for feature in self.features:
feature = feature[0]
if feature in sent['nouns'] or feature in sent['noun_phrases']:
for index in xrange(len(sent['tags'])):
(w, t) = sent['tags'][index]
if w.find(feature.split()[0]) > -1:
JJ = self.get_nearest_JJ(sent['tags'], index)
self.feature_sentences[sent_index]['opinion_sent'].append((feature, JJ))
self.opinion_sentences.append((feature, JJ))
def __init_corpora(self):
self.negation_words = WordListCorpusReader('../data/corpora/', 'negation_words')
self.sent_ends = WordListCorpusReader('../data/corpora', 'sent_ends')
self.negative_sentiments = WordListCorpusReader('../data/corpora/sentiment-lexicon', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader('../data/corpora/sentiment-lexicon', 'positive-words.txt')
def remove_uncertain_features(self):
None
"""
Todo: concat consecutive JJ's (Opt.)
Remove meaningless JJ's (95% done.)
Implement lemmatizing while checking JJ's
Stop scanning for JJ's, after the period or ',' or other sentence ends (done.)
Negation of opinions. (done.)
(Opt.) Append (RR, RB) to the JJ
Special treatment for NOUNS in pros
Fix neg bug
"""
def get_nearest_JJ(self, tags, n_index):
adj = ''
neg = ''
sentiment = None
for i in xrange(n_index + 1, len(tags)):
(w, t) = tags[i]
if w in self.sent_ends.words():
break
if w in self.negation_words.words():
neg = w
if t in ['JJ', 'JJR', 'JJS']:
adj = w
if unicode.encode(w) in self.negative_sentiments.words():
adj = w
sentiment = False
if unicode.encode(w) in self.positive_sentiments.words():
adj = w
sentiment = True
break
start = n_index
if len(adj) < 1:
end = -1
neg = ''
else:
end = n_index - (i - n_index) - 1
for j in xrange(start, end, -1):
(w, t) = tags[j]
if w in self.sent_ends.words():
break
if w in self.negation_words.words():
neg = w
if t in ['JJ', 'JJR', 'JJS']:
adj = w
if unicode.encode(w) in self.negative_sentiments.words():
adj = w
sentiment = False
if unicode.encode(w) in self.positive_sentiments.words():
adj = w
sentiment = True
break
if len(neg) > 1:
sentiment = not sentiment
return (sentiment, neg, adj)
def __init_corpora(self):
self.negation_words = WordListCorpusReader('../data/corpora/', 'negation_words')
self.sent_ends = WordListCorpusReader('../data/corpora', 'sent_ends')
self.negative_sentiments = WordListCorpusReader('../data/corpora/sentiment-lexicon', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader('../data/corpora/sentiment-lexicon', 'positive-words.txt')
def init_corpus(self):
self.negation_words = WordListCorpusReader('../data/corpus/', 'negation-words.txt')
self.negative_sentiments = WordListCorpusReader('../data/corpus/', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader('../data/corpus/', 'positive-words.txt')
class OpinionSentenceCollector:
def __init__(self, features, feature_sentences):
self.features = features
self.feature_sentences = feature_sentences
self.opinion_sentences = []
self.opinion_features = []
self.init_corpus()
for sentence_index in xrange(len(self.feature_sentences)):
sentence = self.feature_sentences[sentence_index]
self.feature_sentences[sentence_index]['opinion_sentence'] = []
for feature in self.features:
#Extracting the feature from (feature, count) tuple
feature = feature[0]
if feature in sentence['nouns'] or feature in sentence['noun_phrases']:
for tag_index in xrange(len(sentence['tags'])):
(word, tag) = sentence['tags'][tag_index]
if(word.find(feature.split()[0])) > -1:
(sentiment_score, opinion) = self.calculate_sent_score(sentence['tags'], tag_index)
if len(opinion) > 0:
self.opinion_features.append(feature)
self.opinion_sentences.append((feature, sentiment_score, sentence['sentence']))
def init_corpus(self):
self.negation_words = WordListCorpusReader('../data/corpus/', 'negation-words.txt')
self.negative_sentiments = WordListCorpusReader('../data/corpus/', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader('../data/corpus/', 'positive-words.txt')
def calculate_sent_score(self, tags, tag_index):
positive_sentiment_score = 0
negative_sentiment_score = 0
adjective = ''
negation_words = ''
for i in xrange(tag_index + 1, len(tags)):
(word, tag) = tags[i]
if word in self.negation_words.words():
negation_words = word
if tag in ['JJ', 'JJR', 'JJS']:
adjective = word
if word in self.negative_sentiments.words():
adjective = word
if not len(negation_words) > 0:
negative_sentiment_score += 1
else:
positive_sentiment_score += 1
if word in self.positive_sentiments.words():
adjective = word
if not len(negation_words) > 0:
positive_sentiment_score += 1
else:
negative_sentiment_score += 1
start = 0
negation_words = ''
for j in xrange(start, tag_index):
(word, tag) = tags[j]
if word in self.negation_words.words():
negation_words = word
if tag in ['JJ', 'JJR', 'JJS']:
adjective = word
if word in self.negative_sentiments.words():
adjective = word
if not len(negation_words) > 0:
negative_sentiment_score += 1
else:
positive_sentiment_score += 1
if word in self.positive_sentiments.words():
if not len(negation_words) > 0:
positive_sentiment_score += 1
else:
negative_sentiment_score += 1
final_score = positive_sentiment_score - negative_sentiment_score
#print "Sentiment Score", final_score, adjective
return final_score, adjective
delete = (config.has_option('cooking', 'delete')
and config.get('cooking', 'delete').split(REGEX_SEPARATOR)) or ''
keywords = (config.has_option('cooking', 'keywords') and config.get(
'cooking', 'keywords').lower().split(',')) or 'movistar'
oficial_users = (config.has_option('cooking', 'oficial_users') and config.get(
'cooking', 'oficial_users').lower().split(',')) or 'movistar'
languages = (config.has_option('cooking', 'languages') and config.get(
'cooking', 'languages').lower().split(',')) or 'spanish'
steps = config.get('cooking', 'steps').lower().split(',')
text_field_out = (config.has_option('cooking', 'text_field_out')
and config.get('cooking', 'text_field_out').lower()) or ''
text_field_in = (config.has_option('cooking', 'text_field_in')
and config.get('cooking', 'text_field_in').lower()) or ''
# Loading serving section
output_fields = (config.has_option('serving', 'output_fields') and config.get(
'serving', 'output_fields').lower().split(',')) or ''
output_separator = (config.has_option('serving', 'output_separator')
and config.get('serving', 'output_separator')) or ','
# Reading corpus
data.path = [os.getcwd()] + data.path
stopwords = WordListCorpusReader(
data.GzipFileSystemPathPointer('stopwords.zip'), languages)
# Loading dependencies
yamlImport = zipimport.zipimporter('yaml.zip')
yalm = yamlImport.load_module('yaml')
nltkImport = zipimport.zipimporter('nltk.zip')
nltk = nltkImport.load_module('nltk')
from .VerbValencyReader import VerbValencyReader
from .DadeganReader import DadeganReader
from .TreebankReader import TreebankReader
from .WikipediaReader import WikipediaReader
from .SentiPersReader import SentiPersReader
from .TNewsReader import TNewsReader
from .Normalizer import Normalizer
from .InformalNormalizer import InformalNormalizer, InformalLemmatizer
from .Stemmer import Stemmer
from .Lemmatizer import Lemmatizer
from .SequenceTagger import SequenceTagger, IOBTagger
from .POSTagger import POSTagger, StanfordPOSTagger
from .Chunker import Chunker, RuleBasedChunker, tree2brackets
from .DependencyParser import DependencyParser, MaltParser, TurboParser
from .utils import default_stopwords
from nltk.corpus import WordListCorpusReader
stopwords = WordListCorpusReader('', [default_stopwords], encoding='utf8')
def sent_tokenize(text):
if not hasattr(sent_tokenize, 'tokenizer'):
sent_tokenize.tokenizer = SentenceTokenizer()
return sent_tokenize.tokenizer.tokenize(text)
def word_tokenize(sentence):
if not hasattr(word_tokenize, 'tokenizer'):
word_tokenize.tokenizer = WordTokenizer()
return word_tokenize.tokenizer.tokenize(sentence)
class OpinionSentenceFinder:
def __init__(self, features, feature_sentences):
self.feature_sentences = feature_sentences
self.opinion_sentences = []
self.features = features
self.__init_corpora()
for sent_index in xrange(len(self.feature_sentences)):
sent = self.feature_sentences[sent_index]
self.feature_sentences[sent_index]['opinion_sent'] = []
for feature in self.features:
feature = feature[0]
if feature in sent['nouns'] or feature in sent['noun_phrases']:
for index in xrange(len(sent['tags'])):
(w, t) = sent['tags'][index]
if w.find(feature.split()[0]) > -1:
JJ = self.get_nearest_JJ(sent['tags'], index)
self.feature_sentences[sent_index][
'opinion_sent'].append((feature, JJ))
self.opinion_sentences.append((feature, JJ))
def __init_corpora(self):
self.negation_words = WordListCorpusReader('../data/corpora/',
'negation_words')
self.sent_ends = WordListCorpusReader('../data/corpora', 'sent_ends')
self.negative_sentiments = WordListCorpusReader(
'../data/corpora/sentiment-lexicon', 'negative-words.txt')
self.positive_sentiments = WordListCorpusReader(
'../data/corpora/sentiment-lexicon', 'positive-words.txt')
def remove_uncertain_features(self):
None
"""
Todo: concat consecutive JJ's (Opt.)
Remove meaningless JJ's (95% done.)
Implement lemmatizing while checking JJ's
Stop scanning for JJ's, after the period or ',' or other sentence ends (done.)
Negation of opinions. (done.)
(Opt.) Append (RR, RB) to the JJ
Special treatment for NOUNS in pros
Fix neg bug
"""
def get_nearest_JJ(self, tags, n_index):
adj = ''
neg = ''
sentiment = None
for i in xrange(n_index + 1, len(tags)):
(w, t) = tags[i]
if w in self.sent_ends.words():
break
if w in self.negation_words.words():
neg = w
if t in ['JJ', 'JJR', 'JJS']:
adj = w
if unicode.encode(w) in self.negative_sentiments.words():
adj = w
sentiment = False
if unicode.encode(w) in self.positive_sentiments.words():
adj = w
sentiment = True
break
start = n_index
if len(adj) < 1:
end = -1
neg = ''
else:
end = n_index - (i - n_index) - 1
for j in xrange(start, end, -1):
(w, t) = tags[j]
if w in self.sent_ends.words():
break
if w in self.negation_words.words():
neg = w
if t in ['JJ', 'JJR', 'JJS']:
adj = w
if unicode.encode(w) in self.negative_sentiments.words():
adj = w
sentiment = False
if unicode.encode(w) in self.positive_sentiments.words():
adj = w
sentiment = True
break
if len(neg) > 1:
sentiment = not sentiment
return (sentiment, neg, adj)
