基于Python实现虚假评论检测可视化系统
主要代码是参考：https://GitHub.com/SoulDGXu/NLPVisualizationSystem/tree/master/frontend
他这个代码实现了词云、摘要生成等功能吧。因为我做的是虚假评论检测系统，就没有使用他这个里面的功能，参考了他的思路和使用 了他的前端界面。
前端是Bootstrap框架完成的，后端是用的flask和Tensorflow框架。tensorflow框架就是自己算法的主体啦。这里的算法是BERT-whitening+LR实现的，准确率也可以的。通过LR_xitong（）进行的调用。
主要的功能有：登录注册、单条文本检测、批量文本检测、网页评论爬取。
还是有不足的地方，例如爬取只爬取了一页的内容。
1.app.py
这个代码就是Flask的整个逻辑实现的地方啦，通过路由规则到达指定的页面，然后通过get方式得到页面输入的内容，通过post方式返回内容给前端页面。
# -*- coding: utf-8 -*-
"""
服务：
-自动生成词云图：
1. 根据用户输入指定网址，通过采集该网址文本进行处理。
2. 根据用户输入文本字符串进行处理。
3. 根据用户输入载入本地文本进行处理，用户将所需要处理文本文件放入text文本夹中，指定文件名进行处理。
-文本关键信息提取
-文本情感分析
-用户评价分析
-用户画像
后台设计：
1. 服务接口设计
1.1 页面请求设计
1.2 数据请求设计
2. 异常请求设计
"""
import os
from src import config
from src.exe import LR_xitong
from src.exe import file
from src.exe import yelp_claw
from flask import Flask, render_template,send_from_directory
from flask import Flask, render_template, request, redirect, url_for
from flask import request, redirect, JSON, url_for
from werkzeug.utils import secure_filename
import requests
import json
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import and_
# from src.exe import exe_02
# from src.exe import exe_03
# from src.exe import exe_05
# from src.exe import exe_06
# from src.exe import exe_01, exe_02, exe_03, exe_05, exe_06
## =================================== 路由配置 ===================================
##############################################################################################
print(LR_xitong.predict_review())
## Part 1 ++++++++++++++++++++++++++++++++++++++++++++++++++++
#==================================================================
#登录,连接数据库
app = Flask(__name__, template_folder=config.template_dir,static_folder=config.static_dir)
HOSTNAME = "127.0.0.1"
PORT = 3306
USERNAME = "root"
PASSWord = "root"
DATABASE = "database_learn"
app.config[
'SQLALCHEMY_DATABASE_URI'] = \
f"Mysql+pymysql://{USERNAME}:{PASSWORD}@{HOSTNAME}:{PORT}/{DATABASE}?charset=utf8mb4"
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True
db = SQLAlchemy(app)
@app.route("/")
def index():
return render_template("reGISter.html")
class User(db.Model):
__tablename__ = 'user_list1' #（设置表名）
id = db.Column(db.Integer, primary_key=True) #（设置主键）
username = db.Column(db.String(255), unique=True)
password = db.Column(db.String(255), unique=True)
# 返回一个可以用来表示对象的可打印字符串：（相当于java的toString）
def __repr__(self):
return '<User 用户名：%r 密码：%r>' % (self.username, self.password)# 操作数据库
#增
def add_object(user):
db.session.add(user)
db.session.commit()
print("添加 % r 完成" % user.__repr__)
with app.app_context():
user = User()
user = db.session.merge(user) # 将未绑定的实例或对象合并到会话中
# user.username = 'li三'
# user.password = '123456'
# add_object(user)
# 查 （用到and的时候需要导入库from sqlalchemy import and_）
# def query_object(user, query_condition_u, query_condition_p):
# result = user.query.filter(and_(user.username == query_condition_u, user.password == query_condition_p))
# print("查询 % r 完成" % user.__repr__)
# return result
# 删
# def delete_object(user):
# result = user.query.filter(user.username == '11111').all()
# db.session.delete(result)
# db.session.commit()
# #改
# def update_object(user):
# result = user.query.filter(user.username == '111111').all()
# result.title = 'success2018'
@app.route("/login",methods=['POST'])
def login():
username1=request.fORM.get("username")
password1 = request.form.get("password")
if user.query.filter_by(username =username1,password =password1).all()!=[]:
# print(user.username,username1,user.password,password1)
print("登录成功")
return render_template("text_classification1.html")
else:
print("失败")
print(username1,password1)
return render_template("register.html")
#===========================================================
#注册：
@app.route("/register",methods=['POST'])
def register():
username1=request.form.get("username")
password1 = request.form.get("password")
#判断是否在表中，如果不在，则增加，如果在，则返回已经存在的错误提示
if user.query.filter_by(username=username1, password=password1).all() == []:
user.username = username1
user.password = password1
add_object(user)
return render_template("login.html")
else:
print("已经注册过了")
message="已经注册过了"
return render_template("register.html",message=message)
## Part 2 自动生成词云图 ++++++++++++++++++++++++++++++++++++++++++++++++++++
def read_file(filepath):
"""
Read the local file and transform to text.
Parameters
----------
filepath : TYPE-str
DESCRIPTION: the text file path.
Returns
-------
content : TYPE-str
DESCRIPTION:The preprocessed news text.
"""
f = open(filepath,'r',encoding='utf-8')
content = f.read()
f.close()
return content
def save_to_file(filepath, content):
f = open(filepath, 'w', encoding='utf-8')
f.write(content)
f.close()
def check_url(url):
"""
Check if the URL can be accessed normally.
Open a simulated browser and visit.
If the access is normal, the output is normal, and the error is output.
Parameters
----------
url : TYPE-str
DESCRIPTION: the URL.
Returns
-------
content : TYPE-str
DESCRIPTION:The preprocessed news text.
"""
import urllib
import time
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/49.0.2')] #Mozilla/5.0 (windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0
url = url.replace('\n','').strip()
try:
opener.open(url)
print(url + ' successfully accessed.')
return True
except urllib.error.HttpError:
print(url + ' = Error when accessing the page.')
time.sleep(2)
except urllib.error.URLError:
print(url + " = Error when accessing the page.")
time.sleep(2)
time.sleep(0.1)
return False
##############################################################################################
##############################################################################################
## Part 3 文本预处理
## Part 3.2 文本关键信息提取--多文本分析--主题分析
##############################################################################################
## Part 4 文本分类
#/classification_1是单文本
#英文
@app.route("/classification_1",methods=['GET'])
def review_classification_home():
return render_template("text_classification1.html")
@app.route("/classification_1",methods=['POST'])
def review_classification_input():
text=request.form.get('inputtext')
text1=text #将输入的文本储存到text1中
if not text.isascii(): #如果不是英文
url = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule'
data = {
'i': text,
'from': 'AUTO',
'to': 'AUTO',
'smartresult': 'dict',
'client': 'fanyideskWEB',
'salt': '16071715461327',
'sign': 'f5d5d5c129878e8e36558fb321b16f85',
'ts': '1607171546132',
'bv': 'd943a2cf8cbe86fb2d1ff7fcd59a6a8c',
'doctype': 'json',
'version': '2.1',
'keyfrom': 'fanyi.web',
'action': 'FY_BY_REALTlME',
'typoResult': 'false'
}
# 发送POST请求并获取响应数据
response = requests.post(url, data=data)
result = json.loads(response.text)
# 解析翻译结果并输出
translate_result = result['translateResult'][0][0]['tgt']
print("翻译结果：", translate_result)
text = translate_result
try:
if text!=None:
save_to_file(config.classificaion_input_text_path,text) #英文文本
save_to_file(config.classificaion_input_text1_path,text1) #输入的中文文本
print(text)
return redirect('/download_classification')
except:
return render_template("text_classification1.html")
####################################################################################
#####################################################################################
# 文本分类结果
@app.route('/download_classification', methods=['GET'])
def review_classification():
cur = LR_xitong.predict_review()
print("要返回结果啦")
return render_template("classification.html", curinput=cur)
# 文本分类结果，下载输出结果
@app.route('/download_classification', methods=['POST'])
def download_review_classification():
file_dir, filename = os.path.split(config.download_classification_input_text_save_path)
print("要保存啦")
return send_from_directory(file_dir, filename, as_attachment=True)
######################################################################################
#批量文本处理
@app.route("/classification_2",methods=['GET'])
def pilialng():
return render_template("text_classification2.html")
@app.route('/classification_2', methods=['POST'])
def get_import_file():
userfile = request.files.get('loadfile')
if userfile:
filename = secure_filename(userfile.filename)
types = ['xlsx', 'csv', 'xls']
if filename.split('.')[-1] in types:
uploadpath = os.path.join(config.save_dir, filename)
userfile.save(uploadpath)
save_to_file(config.wc_input_file_save_path, uploadpath)
print('文件上传成功')
return redirect('/download_classification_2')
else:
return render_template("text_classification2.html")
#=============================
#批量文本下载
@app.route('/download_classification_2', methods=['GET'])
def rt_keyinfo_import_file():
filepath=read_file(config.wc_input_file_save_path)
cur = file.predict(filepath) #这里就要把列表的东西返回
return render_template("classification2.html", curinput=cur)
# 03 tab3关键信息生成-下载输出结果
@app.route('/download_classification_2', methods=['POST'])
def download_keyinfo_3():
file.save()
return 0
##############################################################################################
#输入URL
@app.route("/classification_3", methods=['GET'])
def keyinfo_home_1():
return render_template("text_classification3.html")
# 01 tab1关键信息提取构建-获取前端输入数据
@app.route('/classification_3', methods=['POST'])
def get_keyinfo_url():
url = request.form.get('texturl')[25:]
try:
save_to_file(config.keyinfo_input_url_path, url)
# if check_url(url):
# save_to_file(config.keyinfo_input_url_path, url)
# print('add URL: ' + url)
return redirect('/download_classification_3')
except:
return render_template("text_classification3.html")
# 01 tab1关键信息生成-数据请求
@app.route('/download_classification_3', methods=['GET'])
def rt_keyinfo_url():
res_name=read_file(config.keyinfo_input_url_path) #这是读的餐厅名字
#然后进行爬取，存储到另一个路径
yelp_claw.claw(res_name)
cur = file.predict('yelp_reviews.csv')
return render_template("classification3.html", curinput=cur)
# 01 tab1关键信息生成-下载输出结果
@app.route('/download_classification_3', methods=['POST'])
def download_keyinfo_1():
file_dir, filename = os.path.split(config.download_keyinfo_input_url_save_path)
return send_from_directory(file_dir, filename, as_attachment=True)
##############################################################################################
# ############################# 异常处理 ###########################
# 403错误
@app.errorhandler(403)
def miss(e):
return render_template('error-403.html'), 403
# 404错误
@app.errorhandler(404)
def error404(e):
return render_template('error-404.html'), 404
# 405错误
@app.errorhandler(405)
def erro405r(e):
return render_template('error-405.html'), 405
# 500错误
@app.errorhandler(500)
def error500(e):
return render_template('error-500.html'), 500
# 主函数
if __name__ == "__main__":
app.run() 2.LR_xitong.py
这部分代码就是单条文本检测的实现了，先将数据集进行训练，保存LR模型参数，然后使LR对新得到的句子向量进行判断。
## 基础函数库
import numpy as np
## 导入逻辑回归模型函数
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn import linear_model
from src.exe import Singlesentence
from Singlesentence import *
import tensorflow as tf
from tensorflow import keras
##Demo演示LogisticRegression分类
## 构造数据集
train_data_features=pd.read_csv(r'D:\BaiduNetdiskDownload\yelp\new\BHAN+W\res.csv') #需要加一行数组标
file_name = r'D:\BaiduNetdiskDownload\yelp\yelp_rzj\label.csv' #键入训练数据名
label_name = 'label1' #键入标签列标题
#提取评论标签
def getLabel():
df_data=pd.read_csv(file_name, encoding='utf-8')
data = list(df_data[label_name])
return data
label = getLabel()
x_fearures = train_data_features
y_label = label
## 调用逻辑回归模型
lr_clf = LogisticRegression()
## 用逻辑回归模型拟合构造的数据集
lr_clf = lr_clf.fit(x_fearures, y_label)
def predict_review():
x_fearures_new1=[vec()]
##在训练集和测试集上分布利用训练好的模型进行预测
y_label_new1_predict=lr_clf.predict(x_fearures_new1)
if y_label_new1_predict[0] == 1:
a='真实'
else:
a='虚假'
print('The New point 1 predict class:\n',a)
##由于逻辑回归模型是概率预测模型（前文介绍的p = p(y=1|x,\theta)）,所有我们可以利用predict_proba函数预测其概率
y_label_new1_predict_proba=lr_clf.predict_proba(x_fearures_new1)
print('The New point 1 predict Probability of each class:\n',y_label_new1_predict_proba)
a1=read_file(config.classificaion_input_text_path) #
b=read_file(config.classificaion_input_text1_path)
if a1==b:
inputtext=a1
else:
inputtext=b
curinput={'inputtext':inputtext,'a':a,'proba':y_label_new1_predict_proba}
return curinput
3.singleSentence.py
这部分就是对文本通过BERT-whitening模型进行向量化。
#! -*- coding: utf-8 -*-
# 简单的线性变换（白化）操作，就可以达到甚至超过BERT-flow的效果。
from utils import *
import os, sys
import numpy as np
import xlsxwriter
import re
from src import config
import pandas as pd
import tensorflow as tf
from tensorflow import keras
def save_to_file(filepath, content):
"""
Write the text to the local file.
Parameters
----------
filepath : TYPE-str
DESCRIPTION: the file save path.
Returns
-------
content : TYPE-str
DESCRIPTION: the text.
"""
f = open(filepath, 'w', encoding='utf-8')
f.write(content)
f.close()
def read_file(filepath):
"""
Read the local file and transform to text.
Parameters
----------
filepath : TYPE-str
DESCRIPTION: the text file path.
Returns
-------
content : TYPE-str
DESCRIPTION:The preprocessed news text.
"""
f = open(filepath,'r',encoding='utf-8')
content = f.read()
f.close()
return content
def load_mnli_train_data1(filename):
df = pd.read_csv(filename, encoding='gbk')
# 划分data与label
data = df['comment_text']
D = []
with open(filename, encoding='gbk') as f:
for i, l in enumerate(f):
if i > 0:
l = l.strip().split(',')
pattern = r'\.|\?|\~|!|。|、|；|‘|'|【|】|·|!|…|（|）'
result_list = re.split(pattern, data[i-1])
for text in result_list:
D.append((text, l[-1]))
return D
def convert_to_ids1(data, tokenizer, maxlen=64):
"""转换文本数据为id形式
"""
a_token_ids= []
for d in tqdm(data):
token_ids = tokenizer.encode(d, maxlen=maxlen)[0]
a_token_ids.append(token_ids)
a_token_ids = sequence_padding(a_token_ids)
return a_token_ids
def convert_to_vecs1(data, tokenizer, encoder, maxlen=64):
"""转换文本数据为向量形式
"""
a_token_ids = convert_to_ids1(data, tokenizer, maxlen)
with session.as_default():
with session.graph.as_default():
a_vecs = encoder.predict([a_token_ids,
np.zeros_like(a_token_ids)],
verbose=True)
return a_vecs
config1 = tf.ConfigProto(
device_count={'CPU': 1},
intra_op_parallelism_threads=1,
allow_soft_placement=True
)
session = tf.Session(config=config1)
keras.backend.set_session(session)
#BERT配置
config_path = r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\bert_config.json'
checkpoint_path =r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\bert_model.ckpt'
dict_path = r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\vocab.txt'
# 建立分词器
tokenizer = get_tokenizer(dict_path)
# 建立模型
encoder = get_encoder(config_path, checkpoint_path)
# 加载NLI预训练权重
encoder.load_weights('D:\downloads\BERT-whitening-main\BERT-whitening-main\eng\weights\_res200.weights')
def vec():
data=read_file(config.classificaion_input_text_path)
print("在vec函数内的",data)
# pattern = r'\.|\?|\~|!|。|、|；|‘|'|【|】|·|!|…|（|）'
# result_list = re.split(pattern, data)
# D1=[]
# for text in result_list:
# D1.append(text)
# nli_data = D1
nli_data = data
#在这里增加对不符合正常逻辑的句子的判断？还是去除停用词比较好呢？
nli_a_vecs= convert_to_vecs1(
nli_data, tokenizer, encoder
)
# nli_a_vecs=nli_a_vecs.reshape((2,384))
#得到白化后的向量
kernel, bias = compute_kernel_bias([nli_a_vecs],n_components=200)
# np.save('weights/hotel.kernel.bias' , [kernel, bias])
kernel = kernel[:, :768]
a_vecs = transform_and_normalize(nli_a_vecs, kernel, bias) #shape=[8000,768]
#需要在这里将[句子数量，768]变成[1,768]
a=[0]*200#200是这个最后的向量维度
for i in a_vecs:
a=a+i
output = a/len(a_vecs)
return output
4.批量文本的处理
这部分代码和上面单条文本的很像，不同之处就是在predict()函数那里增加了读取文件的操作，将对单文本进行文本向量化变成了对多文本进行文本向量化。
#! -*- coding: utf-8 -*-
# 简单的线性变换（白化）操作，就可以达到甚至超过BERT-flow的效果。
from utils import *
import os, sys
import numpy as np
import xlsxwriter
import re
from src import config
import pandas as pd
import tensorflow as tf
from tensorflow import keras
def save_to_file(filepath, content):
"""
Write the text to the local file.
Parameters
----------
filepath : TYPE-str
DESCRIPTION: the file save path.
Returns
-------
content : TYPE-str
DESCRIPTION: the text.
"""
f = open(filepath, 'w', encoding='utf-8')
f.write(content)
f.close()
def read_file(filepath):
"""
Read the local file and transform to text.
Parameters
----------
filepath : TYPE-str
DESCRIPTION: the text file path.
Returns
-------
content : TYPE-str
DESCRIPTION:The preprocessed news text.
"""
f = open(filepath,'r',encoding='utf-8')
content = f.read()
f.close()
return content
def load_mnli_train_data2(filename):
# df = pd.read_csv(filename, encoding='gbk')
# 划分data与label
# data = df['comment_text']
D = []
with open(filename, encoding='gbk') as f:
for i, l in enumerate(f):
if i > 0:
D.append(l)
return D
def load_mnli_train_data3(filename):
df = pd.read_csv(filename, encoding='gbk')
data = df['comment_text']
D = []
for d in data:
D.append(d)
return D
def convert_to_ids1(data, tokenizer, maxlen=64):
"""转换文本数据为id形式
"""
a_token_ids= []
for d in tqdm(data):
token_ids = tokenizer.encode(d, maxlen=maxlen)[0]
a_token_ids.append(token_ids)
a_token_ids = sequence_padding(a_token_ids)
return a_token_ids
def convert_to_vecs1(data, tokenizer, encoder, maxlen=64):
"""转换文本数据为向量形式
"""
a_token_ids = convert_to_ids1(data, tokenizer, maxlen)
with session.as_default():
with session.graph.as_default():
a_vecs = encoder.predict([a_token_ids,
np.zeros_like(a_token_ids)],
verbose=True)
return a_vecs
config1 = tf.ConfigProto(
device_count={'CPU': 1},
intra_op_parallelism_threads=1,
allow_soft_placement=True
)
session = tf.Session(config=config1)
keras.backend.set_session(session)
#BERT配置
config_path = r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\bert_config.json'
checkpoint_path =r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\bert_model.ckpt'
dict_path = r'D:\HomeWork\Paper\ZhangRong\BERT\BERT\GLUE\BERT_BASE_DIR\uncased_L-12_H-768_A-12\vocab.txt'
# 建立分词器
tokenizer = get_tokenizer(dict_path)
# 建立模型
encoder = get_encoder(config_path, checkpoint_path)
# 加载NLI预训练权重
encoder.load_weights('D:\downloads\BERT-whitening-main\BERT-whitening-main\eng\weights\_res200.weights')
# 得到向量
def vec1(nli_data):
# 在这里增加对不符合正常逻辑的句子的判断？还是去除停用词比较好呢？
# nli_data = preProcess(nli_data) #先将网页那些去除
nli_a_vecs = convert_to_vecs1(
nli_data, tokenizer, encoder
)
# 得到白化后的向量
kernel, bias = compute_kernel_bias([nli_a_vecs], n_components=200)
# np.save('weights/hotel.kernel.bias' , [kernel, bias])
kernel = kernel[:, :768]
a_vecs = transform_and_normalize(nli_a_vecs, kernel, bias) # shape=[8000,768]
# 需要在这里将[句子数量，768]变成[1,768]
a = [0] * 200 # 200是这个最后的向量维度
for i in a_vecs:
a = a + i
output = a / len(a_vecs)
return output
## 导入逻辑回归模型函数
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn import linear_model
from src.exe import Singlesentence
from Singlesentence import *
import tensorflow as tf
from tensorflow import keras
##Demo演示LogisticRegression分类
## 构造数据集
train_data_features=pd.read_csv(r'D:\BaiduNetdiskDownload\yelp\new\BHAN+W\res.csv') #需要加一行数组标
file_name = r'D:\BaiduNetdiskDownload\yelp\yelp_rzj\label.csv' #键入训练数据名
label_name = 'label1' #键入标签列标题
#提取评论标签
def getLabel():
df_data=pd.read_csv(file_name, encoding='utf-8')
data = list(df_data[label_name])
return data
label = getLabel()
x_fearures = train_data_features
y_label = label
## 调用逻辑回归模型
lr_clf = LogisticRegression()
## 用逻辑回归模型拟合构造的数据集
lr_clf = lr_clf.fit(x_fearures, y_label)
def predict(filepath):
Data = []
#开始预测
data = load_mnli_train_data3(filepath)
for input_text in data:
#进行预处理，去掉<br>和索引号
input_text = re.sub(r"'", "", input_text)
input_text = re.sub(r"[^a-zA-Z0-9\s]", "", input_text)
predict=lr_clf.predict([vec1(input_text)])
if predict[0] == 1:
a = '真实'
Data.append([input_text,a])
else:
b = '虚假'
Data.append([input_text,b])
curinput={'Data':Data,'filename':filepath,'url':read_file(config.keyinfo_input_url_path) }
print(Data)
return curinput
# predict()
# def save():
# # 将data内容写到表格中
# dd=pd.DataFrame(predict().Data,columns=['comment','label'])
# file='D:\downloads\predict_file.csv'
# dd.to_csv(file)
# return file
#5.爬取网页代码
import requests
import csv
# 设置 api 访问密钥和 API 端点 URL
# API_KEY = 'GET https://api.yelp.com/v3/businesses/north-india-restaurant-san-francisco/reviews'
# API_HOST = 'https://api.yelp.com/v3'
# REVIEWS_PATH = '/businesses/{}/reviews'
#
# # 设置餐厅ID和请求头
# business_id = 'NORTH-INDIA-RESTAURANT-SAN-FRANCISCO'
# headers = {'Authorization': 'Bearer %s' % API_KEY}
#
# # 发送评论请求获取餐厅评论
# url = API_HOST + REVIEWS_PATH.format(business_id)
#通过请求分析得到店铺的评论接口，然后进行爬取解析Json对象得到想要的内容和特征
def claw(res_name):
# businessid=res_name
i=0
print(res_name+"这是店铺名称")
response = requests.get('https://www.yelp.com/biz/{}/review_feed?start={}'.format(res_name,i))
reviews = response.json()['reviews']
# 将评论数据写入 CSV 文件
with open('yelp_reviews.csv', mode='w', encoding='utf-8', newline='') as file:
writer = csv.writer(file)
writer.writerow(['User Name', 'User_URL', 'Review Data', 'Rating', 'comment_text', 'Review Count'])
for review in reviews:
user_name = review['user']['altText'] # 用户ID
user_link = review['user']['link'][21:] # 用户个人地址
review_count = review['user']['reviewCount'] # 用户评论数量
rating = review['rating'] # 评论评分
text = review['comment']['text'] # 评论
data = review['localizedDate'] # 拿的评论日期
writer.writerow([user_name, user_link, data, rating, text, review_count])主要代码好像就这么多了。接下来是可视化界面：
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