[PHP] 百度飞桨学习——七日打卡作业(五)大作业

• 爬取任意一期正片视频下评论
• 评论条数不少于1000条
  

• 数据预处理：清理清洗评论中特殊字符（如：@#￥%、emoji表情符）,清洗后结果存储为txt文档
• 中文分词：添加新增词（如：青你、奥利给、冲鸭），去除停用词（如：哦、因此、不然、也好、但是）
• 统计top10高频词
• 可视化展示高频词
  

• 根据词频生成词云
• 可选项-添加背景图片，根据背景图片轮廓生成词云
  

• 中文分词需要jieba
• 词云绘制需要wordcloud
• 可视化展示中需要的中文字体
• 网上公开资源中找一个中文停用词表
• 根据分词结果自己制作新增词表
• 准备一张词云背景图（附加项，不做要求，可用hub抠图实现）
• paddlehub配置
  

!pip install jieba
!pip install wordcloud -i https://pypi.tuna.tsinghua.edu.cn/simple

# Linux系统默认字体文件路径
!ls /usr/share/fonts/
# 查看系统可用的ttf格式中文字体
!fc-list :lang=zh | grep ".ttf"

!wget https://mydueros.cdn.bcebos.com/font/simhei.ttf # 下载中文字体
#创建字体目录fonts
!mkdir .fonts
# 复制字体文件到该路径
!cp simhei.ttf .fonts/

#安装模型
!hub install porn_detection_lstm==1.1.0
!pip install --upgrade paddlehub

from __future__ import print_function
import requests
import json
import re #正则匹配
import time #时间处理模块
import jieba #中文分词
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
from PIL import Image
from wordcloud import WordCloud  #绘制词云模块
import paddlehub as hub

#请求爱奇艺评论接口，返回response信息
def getMovieinfo(url):
  '''
  请求爱奇艺评论接口，返回response信息
  参数  url: 评论的url
  :return: response信息
  '''
  session = requests.Session()
  headers = {
    "User-Agent":"Mozilla/5.0",
    "Accept":"application/json",
    "Referer":"http://m.iqiyi.com/v_19rqriflzg.html",
    "Origin":"http://m.iqiyi.com",
    "Host":"sns-comment.iqiyi.com",
    "Connection":"keep-alive",
    "Accept-Language":"en-us,en;q=0.9,zh-CN;q=0.8,zh;q=0.7,zh_TW;q=0.6",
    "Accept-Encoding":"gzip,deflate"
  }
  response =session.get(url,headers=headers)
  if response.status_code == 200:
    return response.text
  return None


#解析json数据，获取评论
def saveMovieInfoToFile(lastId,arr):
  '''
  解析json数据，获取评论
  参数  lastId:最后一条评论ID  arr:存放文本的list
  :return: 新的lastId
  '''
  url = "https://sns-comment.iqiyi.com/v3/comment/get_comments.action?agent_type=118&agent_version=9.11.5&business_type=17&content_id=15068699100&page=&page_size=10&types=time&last_id="
  url += str(lastId)
  responseTxt = getMovieinfo(url)
  responseJson = json.loads(responseTxt)
  comments = responseJson['data']['comments']
  for val in comments:
    if 'content' in val.keys():
       # print(val['content'])
      arr.append(val['content'])
    lastId = str(val['id'])
  return lastId

#去除文本中特殊字符
def clear_special_char(content):
  '''
  正则处理特殊字符
  参数 content:原文本
  return: 清除后的文本
  '''
  s = re.sub(r"</?(.+?)>|&nbsp;|\t|\r","",content)
  s = re.sub(r"\n"," ",s)
  s = re.sub(r"\*","\\*",s)
  s = re.sub('[^\u4e00-\u9fa5^a-z^A-Z^0-9]','',s)
  s = re.sub('[\001\002\003\004\005\006\007\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a]+','',s)
  s = re.sub('[a-zA-Z]','',s)
  s = re.sub('^\d+(\.\d+)?$','',s)
  return s

def fenci(text):
  '''
  利用jieba进行分词
  参数 text:需要分词的句子或文本
  return：分词结果
  '''
  jieba.load_userdict("add_words.txt")
  seg = jieba.lcut(text,cut_all=False)
  return seg

def stopwordslist(file_path):
  '''
  创建停用词表
  参数 file_path:停用词文本路径
  return：停用词list
  '''
  stopwords = [line.strip() for line in open(file_path,encoding='UTF-8').readlines()] 
  return stopwords

def movestopwords(sentence,stopwords,counts):
  '''
  去除停用词,统计词频
  参数 file_path:停用词文本路径 stopwords:停用词list counts: 词频统计结果
  return：None
  '''
  out = []
  for word in sentence:
    if word not in stopwords:
      if len(word) != 1:
        counts[word] = counts.get(word,0)+1

  return None

def drawcounts(counts,num):
  '''
  绘制词频统计表
  参数 counts: 词频统计结果 num:绘制topN
  return：none
  '''
  x_aixs =[]
  y_aixs =[]
  c_order = sorted(counts.items(),key=lambda x:x[1],reverse=True)
  for c in c_order[:num]:
    x_aixs.append(c[0])
    y_aixs.append(c[1])
  matplotlib.rcParams['font.sans-serif'] = ['SimHei']
  matplotlib.rcParams['axes.unicode_minus'] = False
  plt.bar(x_aixs,y_aixs)
  plt.title('词频统计结果')
  plt.show()
  #return

def drawcloud(word_f):
  '''
  根据词频绘制词云图
  参数 word_f:统计出的词频结果
  return：none
  '''
  cloud_mask = np.array(Image.open('china.png'))
  st = set(["东西","这是"])
  wc = WordCloud(background_color='white',
  mask=cloud_mask,
  max_words=150,
  font_path='simhei.ttf',
  min_font_size=10,
  max_font_size=100,
  width=400,
  relative_scaling=0.3,
  stopwords=st)
  #wc.fit_words(word_f)
  wc.generate_from_frequencies(word_f)
  wc.to_file('pic.png')

def text_detection(text,file_path):
  '''
  使用hub对评论进行内容分析
  return：分析结果

  '''
  porn_detection_lstm = hub.Module(name="porn_detection_lstm")
  f = open('aqy.txt','r',encoding='utf-8')
  for line in f:
    if len(line.strip())==1:
      continue
    else:
      test_text.append(line)
  f.close()
  input_dict = {"text":test_text}
  results = porn_detection_lstm.detection(data=input_dict,use_gpu=True,batch_size=1)
   # print(results)
  for index,item in enumerate(results):
    if item['porn_detection_key']=='porn':
      print(item['text'],':',item['porn_probs'])

#评论是多分页的，得多次请求爱奇艺的评论接口才能获取多页评论,有些评论含有表情、特殊字符之类的
#num 是页数，一页10条评论，假如爬取1000条评论，设置num=100
if __name__ == "__main__":
  num = 300
  lastId = '0'
  arr = [ ]
  with open('aqy.txt','a',encoding='utf-8') as f:
    for i in range(num):
      lastId = saveMovieInfoToFile(lastId,arr)
      #  for i in arr:
      #    print("arr的内容是：",i)
        ##正常运行
      time.sleep(0.5)
    for item in arr:
      Item = clear_special_char(item)
       # print("Item的内容是",Item)
       # print("Item类型是",type(Item))
      if Item.strip()!='':
        try:
          f.write(Item+'\n')
          #print()
        except  Exception as e:
          print(e)
          #print("含特殊字符")
  print('共取评论:',len(arr))
  f = open('aqy.txt','r',encoding='utf-8')
  counts = {}
  for line in f:
    words = fenci(line)
    stopwords = stopwordslist('cn_stopwords.txt')
    movestopwords(words,stopwords,counts)
  #print("counts 的类型是",type(counts))


  drawcounts(counts,10)
  drawcloud(counts)
  f.close()


  file_path = 'aqy.txt'
  test_text = []
  text_detection(test_text,file_path)
  display(Image.open('pic.png')) #显示生成的词云图像