def weibo_subob_rub_neu_classifier(items, batch=RUBBISH_BATCH_COUNT):
'''
分类主函数:
输入数据:weibo(list元素)，示例：[[mid,text,...],[mid,text,...]...]
batch: rubbish filter的参数
输出数据:label_data(字典元素)，示例：{{'mid':类别标签},{'mid':类别标签}...}
1表示垃圾文本，0表示新闻文本，[2表示中性文本, 已去除]，-1表示有极性的文本
'''
results = []
items = rubbish_classifier(items, batch=batch)
for item in items:
label = 1
if item['rub_label'] == 1:
label = 1 # 垃圾
else:
item = subob_classifier(item)
if item['subob_label'] == 1:
label = 0 # 客观
else:
sentiment = triple_classifier(item)
if sentiment == 0:
# label = 2 # 中性
label = cut_mid_weibo(item['content168'])
else:
label = -1 # 有极性
item['subob_rub_neu_label'] = label
results.append(item)
return results
def classify(weibo, flag):
'''
分类主函数:
输入数据:weibo(list元素)，示例：[[mid,text,...],[mid,text,...]...]
flag(标记变量，任意设置)
输出数据:label_data(字典元素)，示例：{{'mid':类别标签},{'mid':类别标签}...}
1表示垃圾文本，0表示新闻文本，2表示中性文本，-1表示有极性的文本
'''
start = time.time()
label_data = start_ad(weibo, flag) #垃圾分类
end = time.time()
print(end - start)
news_weibo = []
for i in range(0, len(weibo)):
if label_data[str(weibo[i][0])] == 0:
news_weibo.append(weibo[i])
start = time.time()
label = cut_weibo(news_weibo) #规则分类
end = time.time()
print 'cutting weibo by rules takes %s' % (end - start)
start = time.time()
for i in range(0, len(label)):
if label[i] == 0:
mid = news_weibo[i][0]
text = news_weibo[i][1]
sentiment = triple_classifier(text) #调用中性情感分类器
if sentiment == 0:
label_data[str(mid)] = cut_mid_weibo(text)
#label_data[str(mid)] = 2
else:
label_data[str(mid)] = -1
end = time.time()
print 'classifying weibo takes %s' % (end - start)
return label_data
def classify(weibo,flag):
'''
分类主函数:
输入数据:weibo(list元素)，示例：[[mid,text,...],[mid,text,...]...]
flag(标记变量，任意设置)
输出数据:label_data(字典元素)，示例：{{'mid':类别标签},{'mid':类别标签}...}
1表示垃圾文本，0表示新闻文本，2表示中性文本，-1表示有极性的文本
'''
start = time.time()
label_data = start_ad(weibo,flag)#垃圾分类
end = time.time()
print (end-start)
news_weibo = []
for i in range(0,len(weibo)):
if label_data[str(weibo[i][0])] == 0:
news_weibo.append(weibo[i])
start = time.time()
label = cut_weibo(news_weibo)#规则分类
end = time.time()
print 'cutting weibo by rules takes %s' % (end-start)
start = time.time()
for i in range(0,len(label)):
if label[i] == 0:
mid = news_weibo[i][0]
text = news_weibo[i][1]
sentiment = triple_classifier(text)#调用中性情感分类器
if sentiment == 0:
label_data[str(mid)] = cut_mid_weibo(text)
#label_data[str(mid)] = 2
else:
label_data[str(mid)] = -1
end = time.time()
print 'classifying weibo takes %s' % (end-start)
return label_data
