def summary_main(weibo_data): #摘要自动生成主函数
'''
输入数据：
weibo列表：[weibo1,weibo2,...]
'''
word_result, word_weight = word_net(weibo_data, 5)
text_list = text_net(word_result, word_weight, weibo_data)
text_str = ''
for text in text_list:
re_t = re_cut(text)
if not len(re_t):
continue
if re_t[-1] != '。':
text_str = text_str + re_t + '。'
else:
text_str = text_str + re_t
#print text_str
tr4s = TextRank4Sentence()
tr4s.analyze(text=text_str, lower=True, source='all_filters')
result = []
for item in tr4s.get_key_sentences(num=10):
result.append(item.sentence)
return result
def opinion_main(weibo_data,k_cluster):
'''
观点挖掘主函数：
输入数据：
weibo_data：微博列表，[weibo1,weibo2,...]
k_cluster：子话题个数
输出数据：
opinion_name：子话题名称字典，{topic1:name1,topic2:name2,...}
word_result：子话题关键词对，{topic1:[w1,w2,...],topic2:[w1,w2,...],...}
text_list：子话题对应的文本，{topic1:[text1,text2,...],topic2:[text1,text2,..],..}
'''
weibo_new = []
for i in range(0,len(weibo_data)):
text = weibo_data[i]
n = str(text).count('@')
if n >= 5:
continue
value = cut_filter(text)
if len(value) > 0:
if text != '转发微博':
weibo_new.append(value)
word_result,word_weight = word_net(weibo_new,k_cluster)#提取关键词对
text_list,opinion_name = text_net(word_result,word_weight,weibo_new)#提取代表文本
return opinion_name,word_result,text_list
def opinion_main(weibo_data, k_cluster):
'''
观点挖掘主函数：
输入数据：
weibo_data：微博列表，[weibo1,weibo2,...]
k_cluster：子话题个数
输出数据：
opinion_name：子话题名称字典，{topic1:name1,topic2:name2,...}
word_result：子话题关键词对，{topic1:[w1,w2,...],topic2:[w1,w2,...],...}
text_list：子话题对应的文本，{topic1:[text1,text2,...],topic2:[text1,text2,..],..}
'''
print('\t\tGetting keywords...')
limit_num = 30000
weibo_data = weibo_num_limit(weibo_data, limit_num)
# while True:
word_result, word_weight, word_main = word_net(weibo_data,
k_cluster) #提取关键词对
# if len(word_result):
# break
# else:
# print('Cluto wrong!!! Trying again... If you want to stop it, just kill it...')
print('\t\tGetting present text...')
text_list, opinion_name = text_net(word_result, word_weight,
weibo_data) #提取代表文本,会保证每个聚类里面的微博数量是相等的
return opinion_name, word_result, text_list, word_main
def search_weibo_from_word(uidlist,keywords):#第四种策略：先根据BM25检索文本，然后再根据关键词的交集筛选文本
'''
输入数据：
uidlist：uid列表
keywords：keywords列表，热点新闻切词之后的结果
输出数据：
text_list：筛选之后的微博文本
'''
text_list,word_set = get_text_word_by_id(uidlist)#根据uid列表获取对应的文本和分词之后的结果
text_set,word_dict = get_text_by_BM(text_list,word_set,keywords)
n = int(0.5*len(text_set))
if n < 1:
n = 1
result_list = TopkHeap(n)
w_n = int(0.5*len(keywords))
if w_n < 1:
w_n = 1
for i in range(0,len(word_dict)):
words = word_dict[i]
len_n = len(set(words)&set(keywords))
if len_n >= w_n:
result_list.Push((len_n,text_set[i]))
result = result_list.TopK()
text_list = []
for i in range(0,len(result)):
if result[i][1] not in text_list:
text_list.append(result[i][1])
if len(text_list) >= 10:
word_result,word_weight = word_net(text_list,OPINION_CLUSTER)
text_list = text_net(word_result,word_weight,text_list)
result = []
for text in text_list:
s = summary_text(text)
max_r,n = get_s(result,s)
if max_r >= 0.5:
continue
else:
result.append(s)
else:
result = [summary_text(text_list)]
return result
def opinion_main(weibo_data,k_cluster):
'''
观点挖掘主函数：
输入数据：
weibo_data：微博列表，[[mid,text,uid,timetamp,uname,forwarding_count,comment_count],...]
k_cluster：子话题个数
输出数据：
opinion_name：子话题名称字典，{topic1:name1,topic2:name2,...}
word_result：子话题关键词对，{topic1:[w1,w2,...],topic2:[w1,w2,...],...}
text_list：子话题对应的文本，{topic1:[text1,text2,...],topic2:[text1,text2,..],..}
'''
weibo_new = []
for i in range(0,len(weibo_data)):
text = weibo_data[i][1]
mid = weibo_data[i][0]
uid = weibo_data[i][2]
timetamp = weibo_data[i][3]
uname = weibo_data[i][4]
forwarding_count = weibo_data[i][5]
comment_count = weibo_data[i][6]
#print 'text..',text
#print 'type..text..',type(text)
text = text.encode('utf-8')
n = str(text).count('@')
if n >= 5:
continue
value = cut_filter(text)
if len(value) > 0:
if text != '转发微博':
weibo_new.append((value,mid,uid,timetamp,uname,forwarding_count,comment_count))
word_result,word_weight = word_net(weibo_new,k_cluster)#提取关键词对
text_list,opinion_name = text_net(word_result,word_weight,weibo_new)#提取代表文本
return opinion_name,word_result,text_list
def main(flag, k_cluster):
weibo = []
weibo_dict = dict()
reader = csv.reader(file('./test/weibo%s.csv' % flag, 'rb'))
for mid, text in reader:
n = str(text).count('@')
if n >= 5:
continue
value = cut_filter(text)
if len(value) > 0:
if text != '转发微博':
weibo.append(str(mid))
weibo_dict[str(mid)] = str(text)
test(weibo, weibo_dict, flag) #生成测试数据
lable = choose_ad(flag) #广告过滤
ind, word = word_net(weibo, weibo_dict, lable, flag, k_cluster) #提取关键词对
write(ind, word, flag) #写关键词对
text_net(weibo, weibo_dict, lable, ind, word, flag) #提取代表文本
def main(flag,k_cluster):
weibo = []
weibo_dict = dict()
reader = csv.reader(file('./test/weibo%s.csv' % flag, 'rb'))
for mid,text in reader:
n = str(text).count('@')
if n >= 5:
continue
value = cut_filter(text)
if len(value) > 0:
if text != '转发微博':
weibo.append(str(mid))
weibo_dict[str(mid)] = str(text)
test(weibo,weibo_dict,flag)#生成测试数据
lable = choose_ad(flag)#广告过滤
ind, word = word_net(weibo,weibo_dict,lable,flag,k_cluster)#提取关键词对
write(ind,word,flag)#写关键词对
text_net(weibo,weibo_dict,lable,ind,word,flag)#提取代表文本
