python毕设案例教学: 基于数据挖掘在京东客户评价方面的研究与应用

NeighborMrSun 2023-03-22 发布于湖南

展开全文

前言

大家早好、午好、晚好吖 ❤ ~欢迎光临本文章

开发环境:

Python 3.8
Pycharm

模块使用:

requests >>> pip install requests 第三方模块
csv>>> 内置模块，无需安装

安装模块：win + R 输入cmd 输入安装命令 pip install 模块名

如果出现爆红可能是因为网络连接超时切换国内镜像源

基本思路:

一. 数据来源分析

明确需求:
- 明确采集网站
- 明确采集数据
  
  商品评论相关的信息
分析商品评论相关的信息请求那个链接可以获取

浏览器自带工具: 开发者工具 <一定要分析清楚采集的数据在什么地方>
- 打开开发者工具: F12 / 右键点击检查选择network
- 点击第二页评论内容: 可以直接找到对应数据包

二. 代码实现步骤 --> 实现单页数据采集

发送请求, 模拟浏览器对于url地址发送请求
获取数据, 获取服务器返回响应数据 response
解析数据, 提取我们想要的数据内容 --> 评论信息
保存数据, 把评论信息保存表格文件里面

采集评论数据

导入模块

# 导入数据请求模块 --> 需要安装 pip install requests
import requests
# 导入格式化输出模块 --> 内置模块, 不需要安装
from pprint import pprint
# 导入csv模块
import csv
# 导入时间模块
import time
# 导入随机模块
import random

创建文件

f = open('口红20.csv', mode='a', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=[
    '昵称',
    '商品',
    '评分',
    '购买时间',
    '评论时间',
    'plus会员',
    '内容',
])

写入表头

csv_writer.writeheader()

多页采集

for page in range(0, 20):
    print(f'正在采集第{page}页的数据内容')

延时操作 random.randint(1, 2) --> 随机生成 1或者2

    time.sleep(random.randint(1, 2))

请求链接

    url = 'https://club.jd.com/comment/productPageComments.action?callback=fetchJSON_comment98&productId=100011323932&score=0&sortType=5&page=3&pageSize=10&isShadowSku=0&rid=0&fold=1'

请求参数 --> 字典数据类型

    data = {
        'callback': 'fetchJSON_comment98',
        'productId': '100011323932',
        'score': '0',
        'sortType': '5',
        'page': page,
        'pageSize': '10',
        'isShadowSku': '0',
        'rid': '0',
        'fold': '1',
    }

模拟浏览器 headers

    headers = {
        # user-agent 用户代理 表示浏览器基本上身份信息
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36'
    }

发送请求

    response = requests.get(url=url, params=data, headers=headers)
    print(response.text)
    break

    # # for循环遍历, 一个一个提取列表里面元素
    # for index in response.json()['comments']:
    #     if index['plusAvailable'] == 201:
    #         Vip = '是'
    #     else:
    #         Vip = '不是'
    #     dit = {
    #         '昵称': index['nickname'],
    #         '商品': index['productColor'],
    #         '评分': index['score'],
    #         '购买时间': index['referenceTime'],
    #         '评论时间': index['creationTime'],
    #         'plus会员': Vip,
    #         '内容': index['content'].replace('\n', ''),
    #     }
    #     # 写入数据 字典方式进行保存
    #     csv_writer.writerow(dit)
    #     print(dit)

评论分析

import pandas as pd

df = pd.read_csv('data.csv', encoding='gbk')
df.head()

VipNum = df['plus会员'].value_counts().to_list()
VipType = df['plus会员'].value_counts().index.to_list()

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker

c = (
    Pie()
    .add(
        '',
        [list(z) for z in zip(VipType, VipNum)],
        radius=['40%', '55%'],
        label_opts=opts.LabelOpts(
            position='outside',
            formatter='{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c}  {per|{d}%}  ',
            background_color='#eee',
            border_color='#aaa',
            border_width=1,
            border_radius=4,
            rich={
                'a': {'color': '#999', 'lineHeight': 22, 'align': 'center'},
                'abg': {
                    'backgroundColor': '#e3e3e3',
                    'width': '100%',
                    'align': 'right',
                    'height': 22,
                    'borderRadius': [4, 4, 0, 0],
                },
                'hr': {
                    'borderColor': '#aaa',
                    'width': '100%',
                    'borderWidth': 0.5,
                    'height': 0,
                },
                'b': {'fontSize': 16, 'lineHeight': 33},
                'per': {
                    'color': '#eee',
                    'backgroundColor': '#334455',
                    'padding': [2, 4],
                    'borderRadius': 2,
                },
            },
        ),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title='plus会员分布情况'))
#     .render('pie_rich_label.html')
)
c.render_notebook()

d = (
    Pie()
    .add(
        series_name='plus会员',
        data_pair=[list(z) for z in zip(VipType, VipNum)],
        radius=['50%', '70%'],
        label_opts=opts.LabelOpts(is_show=False, position='center'),
    )
    .set_global_opts(legend_opts=opts.LegendOpts(pos_left='legft', orient='vertical'))
    .set_series_opts(
        tooltip_opts=opts.TooltipOpts(
            trigger='item', formatter='{a} <br/>{b}: {c} ({d}%)'
        ),
        # label_opts=opts.LabelOpts(formatter='{b}: {c}')
    )
)
d.render_notebook()

scoreNum = df['评分'].value_counts().to_list()
scoreType = df['评分'].value_counts().index.to_list()

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker

score = (
    Pie()
    .add(
        '',
        [list(z) for z in zip(scoreType, scoreNum)],
        center=['35%', '50%'],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title='评分分布情况'),
        legend_opts=opts.LegendOpts(pos_left='15%'),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter='{b}: {c}'))

)
score.render_notebook()

# df['购买时间'] = df['购买时间'].str[:-6]
# BuyDateNum = df['购买时间'].value_counts().to_list()
# BuyDateType = df['购买时间'].value_counts().index.to_list()
BuyDateNum = df['购买时间'].str[:-6].value_counts().to_list()
BuyDateType = df['购买时间'].str[:-6].value_counts().index.to_list()

from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType

BuyDate = (
    Bar({'theme': ThemeType.MACARONS})
    .add_xaxis(BuyDateType)
    .add_yaxis('购买时间', BuyDateNum)
    .set_global_opts(
        title_opts={'text': '商品购买时间', 'subtext': '2023年'}
    )
#     .render('bar_base_dict_config.html')
)
BuyDate.render_notebook()

import jieba
string = ' '.join([i for i in df['评论']])
words = jieba.lcut(string)
# 统计词频
word_count = {}
for word in words:
    if word not in word_count:
        word_count[word] = 1
    else:
        word_count[word] += 1
word_list = list(zip(word_count.keys(),word_count.values()))
word_list

df['商品'] = df['商品'].replace(regex='【.*?】', value='')
ShopNum = df['商品'].value_counts().to_list()[:7]
ShopType = df['商品'].value_counts().index.to_list()[:7]
ShopType

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker

c = (
    Bar()
    .add_xaxis(ShopType)
    .add_yaxis('商品', ShopNum, category_gap='60%')
    .set_series_opts(
        itemstyle_opts={
            'normal': {
                'color': JsCode(
                    '''new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                offset: 0,
                color: 'rgba(0, 244, 255, 1)'
            }, {
                offset: 1,
                color: 'rgba(0, 77, 167, 1)'
            }], false)'''
                ),
                'barBorderRadius': [30, 30, 30, 30],
                'shadowColor': 'rgb(0, 160, 221)',
            }
        }
    )
    
    .set_global_opts(
        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
        title_opts=opts.TitleOpts(title='口红款式', subtitle='DIRO'),
    )
)
c.render_notebook()

import pyecharts.options as opts
from pyecharts.charts import WordCloud
wc = (
    WordCloud()
    .add(series_name='词云分析', data_pair=word_list, word_size_range=[6, 66])
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title='词云分析', title_textstyle_opts=opts.TextStyleOpts(font_size=23)
        ),
        tooltip_opts=opts.TooltipOpts(is_show=True),
    )
#     .render('basic_wordcloud.html')
)
wc.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType


c = (
    WordCloud()
    .add('', word_list, word_size_range=[20, 100], shape=SymbolType.DIAMOND)
    .set_global_opts(title_opts=opts.TitleOpts(title='WordCloud-shape-diamond'))
#     .render('wordcloud_diamond.html')
)
c.render_notebook()