redhongx
74 lines
1.7 KiB
Python
74 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import os
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import codecs
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from .. import normal
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from .. import seg
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from ..classification.bayes import Bayes
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data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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'sentiment.marshal')
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class Sentiment(object):
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def __init__(self):
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self.classifier = Bayes()
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def save(self, fname, iszip=True):
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self.classifier.save(fname, iszip)
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def load(self, fname=data_path, iszip=True):
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self.classifier.load(fname, iszip)
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def handle(self, doc):
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words = seg.seg(doc)
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words = normal.filter_stop(words)
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return words
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def train(self, neg_docs, pos_docs):
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data = []
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for sent in neg_docs:
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data.append([self.handle(sent), 'neg'])
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for sent in pos_docs:
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data.append([self.handle(sent), 'pos'])
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self.classifier.train(data)
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def classify(self, sent):
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ret, prob = self.classifier.classify(self.handle(sent))
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if ret == 'pos':
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return prob
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return 1-prob
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classifier = Sentiment()
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classifier.load()
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def train(neg_file, pos_file):
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neg = codecs.open(neg_file, 'r', 'utf-8').readlines()
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pos = codecs.open(pos_file, 'r', 'utf-8').readlines()
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neg_docs = []
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pos_docs = []
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for line in neg:
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neg_docs.append(line.rstrip("\r\n"))
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for line in pos:
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pos_docs.append(line.rstrip("\r\n"))
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global classifier
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classifier = Sentiment()
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classifier.train(neg_docs, pos_docs)
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def save(fname, iszip=True):
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classifier.save(fname, iszip)
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def load(fname, iszip=True):
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classifier.load(fname, iszip)
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def classify(sent):
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return classifier.classify(sent)
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