def seg_sentence(sentence):
sentence_seged=jie_test.cut(sentence.strip())
stopwords=stopwordslist(dir_data+'stopwords/stops.txt')
outstr=''
for word in sentence_seged:
if word not in stopwords:
if word !='\t':
outstr +=word
outstr +=" "
return outstr
text_data=os.listdir(text_data_dir)
for textName in sorted(text_data):
textpath = os.path.join(text_data_dir, textName)
if os.path.isdir(textpath):
label_id = len(labels_index)
labels_index[textName] = label_id
for wordname in sorted(os.listdir(textpath)):
wordpath = os.path.join(textpath, wordname)
if sys.version_info < (3,):
f = open(wordpath)
else:
f = open(wordpath, encoding='gbk',errors='ignore')
text = f.read()
seg_list = jie_test.cut(text.strip().replace(' ', ''), cut_all=False)
new_content = " ".join(seg_list)
# texts.append(new_content)
seg_list1=PinyinHelper.convertToPinyinFromSentence(new_content)
print(seg_list1)
texts.append(seg_list1)
f.close()
labels.append(label_id)
for tes in texts:
print("------>>")
print(tes)
print('Found %s texts.' % len(texts))
tokenizer = Tokenizer(num_words=max_nb_words, filters="",oov_token="unk")
import jie_test
from gensim import corpora,models
train_set=[]
walk=os.walk(dir_data+datasets)
print(walk)
for root,dir,files in walk:
for name in files:
f=open(os.path.join(root,name),
'r',
encoding='utf-8',
errors='ignore')
raw=f.read()
word_list=list(jie_test.cut(raw, cut_all=False))
train_set.append(word_list)
print(word_list)
dic=corpora.Dictionary(train_set)
print(dic)
corpus=[dic.doc2bow(text) for text in train_set]
tfidf=models.TfidfModel(corpus)
corpus_tfidf=tfidf[corpus]
print(corpus_tfidf)
LDA=models.LdaModel(corpus_tfidf,
id2word=dic,
num_topics=10)
corpus_LDA=LDA[corpus_tfidf]
