Python爬虫 爬取B站视频弹幕 + 绘制词云

时间:2022-07-25
本文章向大家介绍Python爬虫 爬取B站视频弹幕 + 绘制词云，主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项，具有一定的参考价值，需要的朋友可以参考一下。

文章目录

利用python爬虫抓取B站视频弹幕数据保存到txt，并绘制词云。

视频链接：https://www.bilibili.com/video/BV1zE411Y7JY

一、分析网页

点击弹幕列表，查看历史弹幕，并选择任意一天的历史弹幕，此时就能找到存储该日期弹幕的ajax数据包，所有弹幕数据放在一个i标签里。

查看请求的相关信息

可以发现Request URL关键就是 oid 和 date 两个参数，date很明显是日期，换日期可以实现翻页爬取弹幕，oid应该是视频标识之类的东西，换个oid可以访问其他视频弹幕页面。

二、获取弹幕数据

本文爬取该视频1月1日到今天8月6日的历史弹幕数据，构造出时间序列：

import pandas as pd

start = '20200101'
end = '20200806'
# 生成时间序列
date_list = [x for x in pd.date_range(start, end).strftime('%Y-%m-%d')]
print(date_list)

运行结果如下：

['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', ... '2020-08-06']

Process finished with exit code 0

爬虫代码如下：

import requests
import pandas as pd
import re
import time
import random
from concurrent.futures import ThreadPoolExecutor
import datetime

user_agent = [
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1",
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",
    "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",
    "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",
    "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5",
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
    "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",
    "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24"
    ]
start_time = datetime.datetime.now()

def  Grab_barrage(date):
    headers = {
        "sec-fetch-dest": "empty",
        "sec-fetch-mode": "cors",
        "sec-fetch-site": "same-site",
        "origin": "https://www.bilibili.com",
        "referer": "https://www.bilibili.com/video/BV1Z5411Y7or?from=search&seid=8575656932289970537",
        "cookie": "_uuid=0EBFC9C8-19C3-66CC-4C2B-6A5D8003261093748infoc; buvid3=4169BA78-DEBD-44E2-9780-B790212CCE76155837infoc; sid=ae7q4ujj; DedeUserID=501048197; DedeUserID__ckMd5=1d04317f8f8f1021; SESSDATA=e05321c1%2C1607514515%2C52633*61; bili_jct=98edef7bf9e5f2af6fb39b7f5140474a; CURRENT_FNVAL=16; rpdid=|(JJmlY|YukR0J'ulmumY~u~m; LIVE_BUVID=AUTO4315952457375679; CURRENT_QUALITY=80; bp_video_offset_501048197=417696779406748720; bp_t_offset_501048197=417696779406748720; PVID=2",
        "user-agent": random.choice(user_agent),
    }
    params = {
        'type': 1,
        'oid': '128777652',
        'date': date
    }
    response = requests.get(url, params=params, headers=headers)
    # print(response.encoding)
    response.encoding = 'utf-8'
    # print(response.text)
    comment = re.findall('<d p=".*?">(.*?)</d>', response.text)
    # print(comment)
    with open('barrages.txt', 'a+') as f:
        for con in comment:
            f.write(con + 'n')
    time.sleep(random.randint(1, 3))

def main():
    with ThreadPoolExecutor(max_workers=4) as executor:
        executor.map(Grab_barrage, date_list)
    delta = (datetime.datetime.now() - start_time).total_seconds()
    print(f'用时：{delta}s')

if __name__ == '__main__':
    url = "https://api.bilibili.com/x/v2/dm/history"
    start = '20200101'
    end = '20200806'
    # 生成时间序列
    date_list = [x for x in pd.date_range(start, end).strftime('%Y-%m-%d')]
    count = 0
    main()

程序运行，成功爬取下弹幕数据并保存到txt。

用时：32.040222s

Process finished with exit code 0

三、绘制词云图

1. 读取txt中弹幕数据

with open('barrages.txt') as f:
    data = f.readlines()
    print(f'弹幕数据：{len(data)}条')

运行结果如下：

弹幕数据：52708条

Process finished with exit code 0

2. Pyecharts 绘制词云

import jieba
import collections
import re
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType
from pyecharts import options as opts
from pyecharts.globals import ThemeType, CurrentConfig

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'

with open('barrages.txt') as f:
    data = f.read()

# 文本预处理  去除一些无用的字符   只提取出中文出来
new_data = re.findall('[u4e00-u9fa5]+', data, re.S)  # 只要字符串中的中文
new_data = " ".join(new_data)

# 文本分词--精确模式分词
seg_list_exact = jieba.cut(new_data, cut_all=True)

result_list = []
with open('stop_words.txt', encoding='utf-8') as f:
    con = f.readlines()
    stop_words = set()
    for i in con:
        i = i.replace("n", "")   # 去掉读取每一行数据的n
        stop_words.add(i)

for word in seg_list_exact:
    # 设置停用词并去除单个词
    if word not in stop_words and len(word) > 1:
        result_list.append(word)
print(result_list)

# 筛选后统计
word_counts = collections.Counter(result_list)
# 获取前100最高频的词
word_counts_top100 = word_counts.most_common(100)
# 可以打印出来看看统计的词频
print(word_counts_top100)

word1 = WordCloud(init_opts=opts.InitOpts(width='1350px', height='750px', theme=ThemeType.MACARONS))
word1.add('词频', data_pair=word_counts_top100,
          word_size_range=[15, 108], textstyle_opts=opts.TextStyleOpts(font_family='cursive'),
          shape=SymbolType.DIAMOND)
word1.set_global_opts(title_opts=opts.TitleOpts('弹幕词云图'),
                      toolbox_opts=opts.ToolboxOpts(is_show=True, orient='vertical'),
                      tooltip_opts=opts.TooltipOpts(is_show=True, background_color='red', border_color='yellow'))
# 渲染在html页面上
word1.render("弹幕词云图.html")

运行效果如下：

3. stylecloud 绘制词云

from stylecloud import gen_stylecloud
import jieba
import re


# 读取数据
with open('barrages.txt') as f:
    data = f.read()

# 文本预处理  去除一些无用的字符   只提取出中文出来
new_data = re.findall('[u4e00-u9fa5]+', data, re.S)
new_data = " ".join(new_data)

# 文本分词
seg_list_exact = jieba.cut(new_data, cut_all=False)

result_list = []
with open('stop_words.txt', encoding='utf-8') as f:
    con = f.readlines()
    stop_words = set()
    for i in con:
        i = i.replace("n", "")   # 去掉读取每一行数据的n
        stop_words.add(i)

for word in seg_list_exact:
    # 设置停用词并去除单个词
    if word not in stop_words and len(word) > 1:
        result_list.append(word)
print(result_list)

gen_stylecloud(
    text=' '.join(result_list),
    size=600,
    collocations=False,
    font_path=r'C:WindowsFontsmsyh.ttc',
    output_name='词云图.png',
    icon_name='fas fa-apple-alt',
    palette='cartocolors.qualitative.Bold_5'
)

运行效果如下：

作者：叶庭云 微信公众号：修炼Python CSDN：https://yetingyun.blog.csdn.net/ 本文仅用于交流学习，未经作者允许，禁止转载，更勿做其他用途，违者必究。 觉得文章对你有帮助、让你有所收获的话，期待你的点赞呀，不足之处，也可以在评论区多多指正。

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