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Extractor.py
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from sshtunnel import SSHTunnelForwarder
from snownlp import SnowNLP
import jieba.analyse
import pymongo
from bson import ObjectId
import re
# 通过ssh跳板登陆mongo
def get_mongodb_client():
ssh_address_or_host = ("39.105.96.64",22) ##服务器地址与ssh_port
ssh_username = 'root' ##登录服务器的用户
ssh_password = 'Mongo729' ##登录服务器的密码
remote_bind_address = ('172.17.241.114', 27017)
#mongo_user = 'xxxx' ## 访问数据库的用户名
# mongo_password = 'xxxxx' #访问数据库的密码
server = SSHTunnelForwarder(
ssh_address_or_host=ssh_address_or_host,
ssh_username = ssh_username,
ssh_password = ssh_password ,
remote_bind_address = remote_bind_address)
server.start()
## 这里一定要填入ssh映射到本地的端口 # 通过xshell端口转发mongdb端口到本地
client = pymongo.MongoClient('127.0.0.1',8888)
db = client.admin
return client
client = get_mongodb_client()
db = client.aituwen
# 评论情感分析
def sentiment(sentence):
sentence = sentence
if sentence != None:
score = SnowNLP(sentence).sentiments
else:
score = 0
return score
"""得到user_likes"""
likes = db['likes']
author_id = {}
#key:author_id;value:target_id
for x in likes.find({}, {"author_id":1,"target_id":1}):
author = x.get("author_id")
target = x.get("target_id")
if author_id.__contains__(str(author)):
author_id[str(author)].append(target)
else:
author_id[str(author)] = [target]
# 写入user_likes
user_likes = db['user_likes']
for key,value in author_id.items():
dic = {'author_id':ObjectId(key),'likes':value}
user_likes.insert_one(dic)
print('user_like is done')
"""得到user_comment"""
comments = db['comments']
num = comments.count()
print(num)
author_id = {}
#key:author_id;value:target_id,content
for x in comments.find({}, {"author_id":1,"target_id":1,"content":1}):
author = x.get("author_id")
target = x.get("target_id")
content = x.get("content")
comment = {'post_id': target, 'content': content}
if author_id.__contains__(str(author)):
author_id[str(author)].append(comment)
else:
author_id[str(author)] = [comment]
# 写入user_comment表
user_comment = db['user_comment']
for key,value in author_id.items():
dic = {'author_id':ObjectId(key),'comment':value}
user_comment.insert_one(dic)
print('user_comment is done')
"""通过user_likes建立用户user_like_tags"""
# 导入标签库——place
place = {}
file = open('place.txt',encoding = 'utf-8')
for line in file.readlines():
line = line.strip()
place[line] = 1
posts = db['posts']
user_tags = db['user_likes_tags']
author_id = {}
id_label = {}
topK = 20
post_tags = {}
#key:author_id;value:likes
# 每一个用户
try:
for x in user_likes.find({}, {"author_id":1,"likes":1}):
author = x.get("author_id")
likes = x.get("likes")
author_id[str(author)] = [likes]
label = {} # 每一个用户对应一个label标签库
# 每一篇帖子
for post in likes:
# 判断 post是否已经出现过；如果已经分词过，则直接用库里存的标签；
if post_tags.__contains__(post):
tags = post_tags.get(post)
else:
t = list(posts.find({"_id":ObjectId(post)},{"title":1,"description":1,"media":1}))
if t == []:
continue
else:
t = t[0]
title = t.get("title")
description = x.get("description")
media = t.get("media")
# 正则匹配获得文本数据
try:
title = ''.join(
re.findall(r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]",
title))
sentence = title + '。'
except:
sentence = ''
for i in range(len(media)):
body = media[i]
body = body.get('body')
if body != None:
body = ''.join(re.findall (r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]", body))
sentence = sentence + body
try:
description = ''.join(re.findall (r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]", description))
except:
pass
if description != None:
sentence = sentence + '。'+ description +'!'
# 得到sentence；让sentence去分词提取标签。在标签中的词则生成词表与用户map；
try:
tagsidf = jieba.analyse.extract_tags(sentence, topK=topK, allowPOS=('ns', 's', 'n', 'vn', 'nsf'))
except:
tagsidf = []
# 得到该句子的标签
tags = list(set(tagsidf))
post_tags[post] = tags
# 存入id_label并计数 id:{label1:1,label2:4,labeln:x}，label出现的次数代表用户喜欢程度高；
for tag in tags:
if place.__contains__(tag):
if label.__contains__(tag):
label[tag] += 1
else:
label[tag] = 1
else:
pass
id_label[ObjectId(author)] = label
# 将帖子存入user_label 存入新的集合中；
except:
pass
for key,values in id_label.items():
if values == {}:
continue
dic = {'author_id':ObjectId(key),'tags':value}
user_tags.insert_one(dic)
print('user_like_tags is done')
"""根据user_comment获得user_likeAndComment标签"""
# 用新的id_label
id_label = {}
try:
for x in user_comment.find({}, {"author_id":1,"comment":1}):
author = x.get("author_id")
post_id = []
comments = x.get("comment")
# 对每一条评论
for comment in comments:
comments_id = comment.get("post_id") # 评论帖子对象
comments_content = comment.get("content") # 评论内容
if sentiment(comments_content) >= 0.5: # 情感分析为正则将用户评论过的帖子加入user_comment-id
post_id.append(comments_id)
label = {} # 每一个用户对应一个label标签库
author_id[str(author)] = post_id
#如果评论和点赞重复了，标签权重增加；
for post in post_id:
# 判断 post是否已经出现过；如果已经分词过，则直接用库里存的标签；
if post_tags.__contains__(post):
tags = post_tags.get(post)
else:
t = list(posts.find({"_id":ObjectId(post)},{"title":1,"description":1,"media":1}))
if t == []:
continue
else:
t = t[0]
title = t.get("title")
description = x.get("description")
media = t.get("media")
# 正则匹配获得文本数据
try:
title = ''.join(
re.findall(r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]",
title))
sentence = title + '。'
except:
sentence = ''
for i in range(len(media)):
body = media[i]
body = body.get('body')
if body != None:
body = ''.join(re.findall (r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]", body))
sentence = sentence + body
try:
description = ''.join(re.findall (r"[\u4e00-\u9fa5-\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]", description))
except:
pass
if description != None:
sentence = sentence + '。'+ description +'!'
# 得到sentence；让sentence去分词提取标签。在标签中的词则生成词表与用户map；
try:
tagsidf = jieba.analyse.extract_tags(sentence, topK=topK, allowPOS=('ns', 's', 'n', 'vn', 'nsf'))
except:
tagsidf = []
# 得到该句子的标签
tags = list(set(tagsidf))
post_tags[post] = tags
# 存入id_label并计数 id:{label1:1,label2:4,labeln:x}，label出现的次数代表用户喜欢程度高；
for tag in tags:
if place.__contains__(tag):
if label.__contains__(tag):
label[tag] += 1
else:
label[tag] = 1
else:
pass
id_label[ObjectId(author)] = label
# 将帖子存入user_label 存入新的集合中；
except:
pass
# 在现有标签库中找用户；如果有则将label叠加；若无，则添加新用户；
for x in user_tags.find({}, {"author_id":1,"tags":1}):
author = x.get("author_id")
tags = x.get("tags") # type:dict
if id_label.__contains__(ObjectId(author)):
label = id_label[author] # label也是个dict
print(label)
# 把 id_label中的标签加入到 tags中；
for key_like,value_like in tags.items():
if label.__contains__(key_like):
label[key_like] += value_like
else:
label[key_like] = value_like
print(label)
else:
id_label[ObjectId(author)] = tags
# 存入数据库
user_likeAndcomment_tags = db['user_likeAndcomment_tags']
for key,values in id_label.items():
if values == {}:
continue
dic = {'author_id':ObjectId(key),'tags':values}
user_likeAndcomment_tags.insert_one(dic)
print('done')