class Sentiment:
def __init__(self):
self.classifier = Bayes()
self.seg = Seg()
self.seg.load('seg.pickle')
def save(self, fname):
self.classifier.save(fname)
def load(self, fname):
self.classifier = self.classifier.load(fname)
def handle(self, doc):
words = self.seg.seg(doc)
words = self.filter_stop(words)
return words
def train(self, neg_docs, pos_docs):
datas = []
for doc in neg_docs:
datas.append([self.handle(doc), 'neg'])
for doc in pos_docs:
datas.append([self.handle(doc), 'pos'])
self.classifier.train(datas)
def classify(self, doc):
ret, prob = self.classifier.classify(self.handle(doc))
if ret == 'pos':
return prob
else:
return 1 - prob
@staticmethod
def filter_stop(words):
return list(filter(lambda x: x not in stop_words, words))
def get_seg(self, fname='seg.pickle'):
seg = Seg()
seg.load(fname)
return seg
yield tmp
tmp = ''
else: # middle
tmp += c
if tmp:
yield tmp
def save():
segger.save()
def load():
segger.load()
if __name__ == '__main__':
# train(fname='/Users/LeonTao/PycharmProjects/snownlp/snownlp/seg/data.txt')
#
# segger.save()
segger.load()
sentence = '中华民族的伟大复兴'
words = list(seg(sentence))
print('/'.join(words))
