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:
到此这篇关于基于python实现虚假评论检测可视化系统的文章就介绍到这了,更多相关Python虚假评论检测系统内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!
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