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))
print_menu()
classifier = None
while (True):
command = input("Enter command:")
command = command.lower()
#Train clause
if command.startswith('t'):
classifier = pp.main()
#Load training clause
elif command.startswith('l'):
print("Loading: ", end='')
if classifier is None:
classifier = Bayes(trained=True)
else:
classifier.load()
#Save training clause
elif command.startswith('s'):
print("Saving: ", end='')
if classifier is not None:
classifier.save()
else:
print("Nothing to save")
#Classify clause
elif command.startswith('c'):
if classifier is None:
print("Load training first")
else:
path = command.split(" ")
if (len(path) < 2):
print("Please enter filepath")
def load_model(self, model_path):
self.model = Bayes.load(model_path)
