def preprocess(self, corpus, language='ko'):
pipeline = None
if language == 'ko':
mecab_path = 'C:\\mecab\\mecab-ko-dic'
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.MeCab(mecab_path),
ptm.helper.POSFilter('NN*'), ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(1, 2),
ptm.helper.StopwordFilter(
file='../stopwords/stopwordsKor.txt'))
elif language == 'en':
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.WordPos(),
ptm.helper.POSFilter('NN*'), ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(1, 2),
ptm.helper.StopwordFilter(
file='../stopwords/stopwordsEng.txt'))
result = pipeline.processCorpus(corpus)
print('== ==')
documents = []
for doc in result:
document = ''
for sent in doc:
document += " ".join(sent)
documents.append(document)
return documents
def vectorizeCaseTwo():
corpus = ptm.CorpusFromFieldDelimitedFile('../data/donald.txt', 2)
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Komoran(),
ptm.helper.POSFilter('NN*'), ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(2, 2),
ptm.helper.StopwordFilter(file='./stopwords/stopwordsKor.txt'))
result = pipeline.processCorpus(corpus)
print('== 형태소 분석 + 명사만 추출 + 단어만 보여주기 + 빈도 분석 ==')
print(result)
print()
print('== ==')
documents = []
for doc in result:
document = ''
for sent in doc:
document += " ".join(sent)
documents.append(document)
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(documents)
print(vectorizer.get_feature_names())
print(X.shape)
print(X.toarray())
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)
print(vectorizer.get_feature_names())
print(len(vectorizer.get_feature_names()))
print(X.toarray())
'정 전 비서관을 "정 과장님"으로 부르며 반말을 하는 남자인데요. 최순실 씨처럼 정 전 비서관을 하대하고 있습니다. 또 청와대 내부 정보를 알고 있는 듯하고 또 인사에까지 개입하려고 하고 있습니다. 그렇기 때문에 정윤회 씨로 추정은 됩니다만 확인은 되지 않습니다.'
]
keyword = keyword_extractor(sents)
for word, r in sorted(keyword.items(), key=lambda x: x[1], reverse=True)[:30]:
print('%8s:\t%.4f' % (word, r))
corpus = ptm.CorpusFromFieldDelimitedFile('./data/donald.txt', 2)
# import nltk
# nltk.download()
# 단어 단위로 분리했으니 이제 stopwords를 제거하는게 가능합니다. ptm.helper.StopwordFilter를 사용하여 불필요한 단어들을 지워보도록 하겠습니다.
# 그리고 파이프라인 뒤에 ptm.stemmer.Porter()를 추가하여 어근 추출을 해보겠습니다.
# 한번 코드를 고쳐서 ptm.stemmer.Lancaster()도 사용해보세요. Lancaster stemmer가 Porter stemmer와 어떻게 다른지 비교하면 재미있을 겁니다.
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Komoran(), ptm.helper.POSFilter('NN*'),
ptm.helper.SelectWordOnly(),
ptm.helper.StopwordFilter(file='./stopwords/stopwordsKor.txt'))
result = pipeline.processCorpus(corpus)
print(result)
print()
documents = []
for doc in result:
document = ''
for sent in doc:
document = " ".join(sent)
documents.append(document)
keyword_extractor1 = ptm.keyword.KeywordExtractionKorean(
min_count, max_length, beta, max_iter, verbose, num_words)
keyword1 = keyword_extractor1(documents)
# ptm.tokenizer.Komoran나 ptm.tokenizer.TwitterKorean을 사용해 형태소 분석이 가능합니다.
# 형태소 분석 이후 품사가 NN으로 시작하는 명사들만 추출하고, 단어만 골라내 출력하도록 해봅시다.
#import nltk
#nltk.download('punkt')
#pipeline = ptm.Pipeline(ptm.splitter.NLTK(), ptm.tokenizer.Komoran(),
# ptm.helper.POSFilter('NN*'),
# ptm.helper.SelectWordOnly(),
# ptm.ngram.NGramTokenizer(3),
# ptm.helper.StopwordFilter(file='./stopwords/stopwordsKor.txt')
# )
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(),
ptm.segmentation.SegmentationKorean(
'../model/korean_segmentation_model.crfsuite'),
ptm.ngram.NGramTokenizer(3),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsKor.txt'))
result = pipeline.processCorpus(corpus)
with io.open("../demofile.csv", 'w', encoding='utf8') as f:
for doc in result:
for sent in doc:
f.write('\t'.join(sent) + "\n")
print('== 문장 분리 + 형태소 분석 + 명사만 추출 + 단어만 보여주기 + 구 추출 ==')
print(result)
print()
import nltk
class EnglishDictionarySentimentAnalyzer:
def __init__(self):
name = 'EnglishDictionarySentimentAnalyzer'
def createDictionary(self):
nltk.download('sentiwordnet')
if __name__ == '__main__':
corpus = ptm.CorpusFromFile('../data/sampleEng.txt')
pipeline = ptm.Pipeline(ptm.splitter.NLTK(),
#ptm.tokenizer.Word(), #ptm.tokenizer.WordPos()
#ptm.helper.StopwordFilter(file='../stopwords/stopwordsEng.txt'),
#ptm.tagger.NLTK(),
ptm.tokenizer.WordPos(),
ptm.lemmatizer.WordNet())
result = pipeline.processCorpus(corpus)
EnglishDictionarySentimentAnalyzer().createDictionary()
for doc in result:
grand_score = 0.0
count = 0
for sent in doc:
for _str in sent:
_str[0]
_str[1]
pos = ''
corpus = ptm.CorpusFromFieldDelimitedFileForClassification(input_file,
delimiter=',',
doc_index=4,
class_index=1,
title_index=3)
tups = corpus.pair_map
class_list = []
for id in tups:
# print(tups[id])
class_list.append(tups[id])
mecab_path = 'C:\\mecab\\mecab-ko-dic'
pipeline = ptm.Pipeline(ptm.splitter.NLTK(),
ptm.tokenizer.MeCab(mecab_path),
ptm.helper.POSFilter('NN*'),
ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(1, 2),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsKor.txt')
)
result = pipeline.processCorpus(corpus)
print('== ==')
with open("./model/id_to_category.json") as handle:
id_to_category = json.loads(handle.read())
# 0 - economy 1 - IT 2 - politics
category = []
documents = []
idx = 0
for doc in result:
document = ''
dump(dataset, open(filename, 'wb'))
print('Saved: %s' % filename)
def load_dataset(self, filename):
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
return loaded_model
if __name__ == '__main__':
_negative_docs = ptm.CorpusFromDirectory('txt_sentoken/neg', True)
_positive_docs = ptm.CorpusFromDirectory('txt_sentoken/pos', True)
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Word(),
ptm.helper.StopwordFilter(file='../stopwordsEng.txt'),
ptm.stemmer.Porter())
_neg_result = pipeline.processCorpus(_negative_docs)
_pos_result = pipeline.processCorpus(_positive_docs)
print(
'== Splitting Sentence + Tokenizing + Stopwords Removal + Stemming : Porter =='
)
print(_neg_result)
print()
negative_docs = list()
for doc in _neg_result:
new_doc = []
for sent in doc:
for _str in sent:
if len(_str) > 0:
import nltk
import treform as ptm
from nltk.draw.tree import draw_trees
from nltk import tree, treetransforms
from copy import deepcopy
pipeline = ptm.Pipeline(ptm.splitter.NLTK(),
ptm.tokenizer.Komoran(),
ptm.syntactic_parser.BeneparSyntacticParser()
)
corpus = ptm.CorpusByDataFrame('../sample_data/parser_sample.txt', '\t', 0, header=False)
#corpus = ptm.CorpusFromFieldDelimitedFile('../sample_data/parser_sample.txt', 0)
print(corpus.docs)
trees = pipeline.processCorpus(corpus)
for tree in trees:
print(tree[0])
t = nltk.Tree.fromstring(tree[0])
draw_trees(t)
import treform as ptm
pipeline = None
corpus = ptm.CorpusFromFieldDelimitedFile('../data/donald.txt', 2)
mecab_path = 'C:\\mecab\\mecab-ko-dic'
mode = 'korean_lemmatizer'
if mode is not 'korean_lemmatizer':
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(),
ptm.tokenizer.MeCab(mecab_path),
#ptm.tokenizer.Komoran(),
ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(1, 2, concat=' '),
ptm.helper.StopwordFilter(file='./stopwords/stopwordsKor.txt'))
else:
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(),
ptm.tokenizer.MeCab(mecab_path),
#ptm.tokenizer.Komoran(),
ptm.lemmatizer.SejongPOSLemmatizer(),
ptm.helper.SelectWordOnly(),
# ptm.ngram.NGramTokenizer(1, 2, concat=' '))
ptm.helper.StopwordFilter(file='./stopwords/stopwordsKor.txt'))
documents = [
'오늘은 비가와서 그런지 매우 우울하다', '시험이 끝나야 놀지 스트레스 받아ㅠㅠ', '행복한 하루의 끝이라 아름답고 좋네!',
'더운날에는 아이스 커피가 최고지~~!'
]
#result = pipeline.processCorpus(corpus)
result = pipeline.processCorpus(documents)
]
return ({key: fdist[key] for key in word_only_keys})
print(str(platform.system()).lower())
if str(platform.system()).lower().startswith('win'):
# Window의 경우 폰트 경로
font_path = 'C:/Windows/Fonts/malgun.ttf'
elif str(platform.system()).lower().startswith('mac'):
#for Mac
font_path = '/Library/Fonts/AppleGothic.ttf'
pipeline = ptm.Pipeline(
ptm.splitter.KoSentSplitter(),
ptm.tokenizer.Komoran(),
#ptm.tokenizer.WordPos(),
ptm.helper.POSFilter('NN*'),
ptm.helper.SelectWordOnly(),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsKor.txt'),
ptm.counter.WordCounter())
corpus = ptm.CorpusFromFile('../data/sampleKor.txt')
result = pipeline.processCorpus(corpus)
print(result)
print()
doc_collection = ''
term_counts = {}
for doc in result:
for sent in doc:
import treform as ptm
corpus = ptm.CorpusFromFile('../sample_data/sampleEng.txt')
#pipeline example 1
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Word(),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsEng.txt'),
ptm.stemmer.Porter())
result = pipeline.processCorpus(corpus)
print(
'== Splitting Sentence + Tokenizing + Stopwords Removal + Stemming : Porter =='
)
print(result)
print()
#pipeline example 2
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Word(),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsEng.txt'),
ptm.stemmer.Lancaster())
result = pipeline.processCorpus(corpus)
print(
'== Splitting Sentence + Tokenizing + Stopwords Removal + Stemming : Lancaster =='
)
print(result)
print()
#pipeline example 3
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(), ptm.tokenizer.Word(),
def preprocessing(self, mode, path, stopword_file, files, is_directory=False, doc_index=-1, max=-1):
util = ptm.Utility()
# mode is either filtered or unfiltered or simple
corpus = []
if mode == 'unfiltered':
# path = '/usr/local/lib/mecab/dic/mecab-ko-dic'
pipeline = ptm.Pipeline(ptm.splitter.KoSentSplitter(),
ptm.tokenizer.MeCab(path),
ptm.lemmatizer.SejongPOSLemmatizer(),
ptm.helper.SelectWordOnly(),
ptm.helper.StopwordFilter(file=stopword_file))
for a_file in files:
if is_directory == True and max == -1:
corpus += ptm.CorpusFromDirectory(a_file).docs
elif is_directory == False and doc_index != -1 and max == -1:
corpus += ptm.CorpusFromFieldDelimitedFile(a_file, doc_index).docs
elif is_directory == False and doc_index == -1 and max == -1:
corpus += ptm.CorpusFromFile(a_file).docs
elif is_directory == False and max > 0:
count = 0
docs = []
for line in open(a_file):
if doc_index != -1:
line = line.split()[doc_index]
if len(line) < 1:
continue
toks = line.split()
if len(toks) > 10:
docs.append(line)
count += 1
if count % 10000 == 0:
print('processing... ' + str(count))
if max < count:
break
corpus = ptm.Corpus(docs)
if type(corpus) != list and len(corpus.docs) > 0 or type(corpus) == list and len(corpus) > 0:
result = pipeline.processCorpus(corpus)
for doc in result:
document = []
for sent in doc:
for word in sent:
document.append(word)
self.documents.append(document)
elif mode == 'filtered':
pipeline = ptm.Pipeline(ptm.tokenizer.Word())
# corpus = ptm.CorpusFromFile('/Data/ko_sns_comments/naver_comments15_16_filtered.txt')
for a_file in files:
if is_directory == True and max == -1:
corpus += ptm.CorpusFromDirectory(a_file).docs
elif is_directory == False and doc_index != -1 and max == -1:
corpus += ptm.CorpusFromFieldDelimitedFile(a_file, doc_index).docs
elif is_directory == False and doc_index == -1 and max == -1:
corpus += ptm.CorpusFromFile(a_file).docs
elif is_directory == False and max > 0:
count = 0
docs = []
for line in open(a_file):
if doc_index != -1:
line = line.split()[doc_index]
if len(line) < 1:
continue
toks = line.split()
if len(toks) > 10:
docs.append(line)
count += 1
if count % 10000 == 0:
print('processing... ' + str(count))
if max < count:
break
corpus = ptm.Corpus(docs)
self.documents = pipeline.processCorpus(corpus)
elif mode == 'jamo_split_unfiltered':
# documents = LineSentence(datapath('/Data/ko_sns_comments/naver_comments15_16_filtered.txt'))
pipeline = ptm.Pipeline(ptm.splitter.KoSentSplitter(),
ptm.tokenizer.MeCab(path),
ptm.lemmatizer.SejongPOSLemmatizer(),
ptm.helper.SelectWordOnly(),
ptm.helper.StopwordFilter(file=stopword_file))
for a_file in files:
if is_directory == True and max == -1:
corpus += ptm.CorpusFromDirectory(a_file).docs
elif is_directory == False and doc_index != -1 and max == -1:
corpus += ptm.CorpusFromFieldDelimitedFile(a_file, doc_index).docs
elif is_directory == False and doc_index == -1 and max == -1:
corpus += ptm.CorpusFromFile(a_file).docs
elif is_directory == False and max > 0:
count = 0
docs = []
for line in open(a_file):
if doc_index != -1:
line = line.split()[doc_index]
if len(line) < 1:
continue
toks = line.split()
if len(toks) > 10:
docs.append(line)
count += 1
if count % 10000 == 0:
print('processing... ' + str(count))
if max < count:
break
corpus = ptm.Corpus(docs)
if type(corpus) != list and len(corpus.docs) > 0 or type(corpus) == list and len(corpus) > 0:
result = pipeline.processCorpus(corpus)
for doc in result:
for sent in doc:
_sent = ''
for word in sent:
_sent += word + ' '
_sent = _sent.strip()
_sent = util.jamo_sentence(_sent)
toks = _sent.split()
if len(toks) > 10:
self.documents.append(toks)
elif mode == 'jamo_split_filtered':
# documents = LineSentence(datapath('/Data/ko_sns_comments/naver_comments15_16_filtered.txt'))
pipeline = ptm.Pipeline(ptm.tokenizer.Word())
for a_file in files:
if is_directory == True and max == -1:
corpus += ptm.CorpusFromDirectory(a_file).docs
elif is_directory == False and doc_index != -1 and max == -1:
corpus += ptm.CorpusFromFieldDelimitedFile(a_file, doc_index)
elif is_directory == False and doc_index == -1 and max == -1:
corpus += ptm.CorpusFromFile(a_file)
elif is_directory == False and max > 0:
count = 0
docs = []
for line in open(a_file):
if doc_index != -1:
line = line.split()[doc_index]
if len(line) < 1:
continue
toks = line.split()
if len(toks) > 10:
docs.append(line)
count += 1
if count % 10000 == 0:
print('processing... ' + str(count))
if max < count:
break
corpus = ptm.Corpus(docs)
if type(corpus) != list and len(corpus.docs) > 0 or type(corpus) == list and len(corpus) > 0:
result = pipeline.processCorpus(corpus)
for doc in result:
_sent = ''
for word in doc:
_sent += word + ' '
_sent = _sent.strip()
_sent = util.jamo_sentence(_sent)
toks = _sent.split()
if len(toks) > 10:
self.documents.append(toks)
elif mode == 'simple':
# documents = LineSentence(datapath('/Data/ko_sns_comments/naver_comments15_16_filtered.txt'))
count = 0
for line in open(files[0], encoding='utf-8'):
if doc_index != -1:
line = line.split()[doc_index]
toks = line.split()
if len(toks) > 10:
self.documents.append(toks)
count += 1
if count % 10000 == 0:
print('processing... ' + str(count))
print('Document size for the total dataset: ' + str(len(self.documents)))
return _train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs, labels
if language == 'en':
_train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs \
= read_english_corpus()
elif language == 'ko':
_train_negative_docs, _train_positive_docs, _test_negative_docs, _test_positive_docs, labels \
= read_korean_corpus()
if language == 'ko':
mecab_path = 'C:\\mecab\\mecab-ko-dic'
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(),
ptm.tokenizer.Komoran(),
#ptm.tokenizer.MeCab(mecab_path),
ptm.helper.POSFilter('NN*|V*|IC*'),
ptm.helper.SelectWordOnly(),
ptm.ngram.NGramTokenizer(1, 2),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsKor.txt'))
elif language == 'en':
pipeline = ptm.Pipeline(
ptm.splitter.NLTK(),
ptm.tokenizer.WordPos(),
ptm.helper.POSFilter('NN*|A*|V*|J*'),
ptm.helper.SelectWordOnly(),
#ptm.ngram.NGramTokenizer(1, 2),
ptm.helper.StopwordFilter(file='../stopwords/stopwordsEng.txt'))
def make_documents(result):
docs = []
