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Update Scraping Bilibili Danmaku (bullet comments).py

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pux36pf8t 2 months ago
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Scraping Bilibili Danmaku (bullet comments).py
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import requests
import re
import pandas as pd
from collections import Counter
import jieba
import wordcloud
def extract_ids_from_url(url, head, output_file='aid.txt'):
"""
从给定的 URL 中提取 IDs 并将其保存到指定的文件中。
参数:
url (str): 要请求的 URL。
head (dict): 请求头，用于发起 HTTP 请求。
output_file (str): 存储提取的 ID 的文件路径，默认为 'aid.txt'。
"""
try:
# 发起 GET 请求
response = requests.get(url=url, headers=head)
# 确保请求成功，状态码在 200 到 299 之间
response.raise_for_status()
# 将响应内容解析为 JSON 格式
data = response.json()
# 检查响应数据是否包含 'data' 和 'result' 键
if 'data' in data and 'result' in data['data']:
items = data['data']['result']
# 提取每个条目的 'id' 字段
ids = [item['id'] for item in items]
# 以追加模式打开文件，并写入每个 ID
with open(output_file, 'a') as file:
for aid in ids:
file.write(f"{aid}\n")
print(f"IDs have been saved to {output_file}")
else:
print("Unexpected response format") # 如果响应格式不符合预期，输出提示信息
except requests.RequestException as e:
# 捕获并打印请求相关的错误
print(f"Request error: {e}")
except KeyError as e:
# 捕获并打印键错误
print(f"Key error: {e}")
except Exception as e:
# 捕获并打印其他类型的异常
print(f"An error occurred: {e}")
def process_urls(urls1, headers1, output_file='aid.txt'):
"""
遍历 URL 列表，并对每个 URL 调用 extract_ids_from_url 函数进行处理。
参数:
urls1 (list): 包含 URL 的列表。
headers1 (dict): 请求头，用于发起 HTTP 请求。
output_file (str): 存储提取的 ID 的文件路径，默认为 'aid.txt'。
"""
for url in urls1:
extract_ids_from_url(url, headers1, output_file)
def process_aid_and_cid(aid_file_path, cid_file_path, headers):
# 打开 aid 文件，并读取其中的所有 aid
with open(aid_file_path, 'r') as file:
aids = [line.strip() for line in file if line.strip()]
count = 0
# 打开 cid 文件（以追加模式），准备写入 cid 数据
with open(cid_file_path, 'a') as file:
# 遍历每个 aid，构造请求 URL 并获取对应的数据
for aid in aids:
url = f'https://api.bilibili.com/x/player/pagelist?aid={aid}'
response = requests.get(url=url, headers=headers).json()
# 遍历响应数据中的每个条目，提取 cid
for item in response.get('data', []):
cid = item['cid']
# 将 cid 写入文件
file.write(f"{cid}\n")
count += 1
# 输出处理进度
print(f"Processed: {count} CIDs")
def remove_duplicates(file_path):
# 读取 cid 文件中的所有 cid
with open(file_path, 'r') as file:
cids = [line.strip() for line in file if line.strip()]
# 使用字典去除重复的 cid
unique_cids = list(dict.fromkeys(cids))
# 将去重后的 cid 写回文件
with open(file_path, 'w') as file:
for cid in unique_cids:
file.write(cid + '\n')
# 输出去重完成的提示
# 调用 remove_duplicates 函数，去除 cid 文件中的重复项
remove_duplicates(cid_file_path)
def fetch_danmu():
# 读取 cid 文件
print("开始爬取弹幕")
with open('cid.txt', 'r') as file:
cids = [line.strip() for line in file if line.strip()]
for cid in cids:
url = f'https://api.bilibili.com/x/v2/dm/web/history/seg.so?type=1&oid={cid}&date=2024-08-31'
response = requests.get(url=url, headers=headers)
response.encoding = 'utf-8'
# 匹配弹幕内容
content_list = re.findall('[\u4e00-\u9fa5]+', response.text)
content = '\n'.join(content_list)
# 将弹幕写入 comment.txt
with open('comment.txt', mode='a', encoding='utf-8') as f:
f.write(content + '\n')
# 定义需要过滤的关键词或短语
keywords_to_remove = [
'出错啦',
'错误号',
'由于触发哔哩哔哩安全风控策略',
'该次访问请求被拒绝'
]
# 定义一个正则表达式模式，用于匹配需要删除的内容
pattern = re.compile('|'.join(re.escape(keyword) for keyword in keywords_to_remove))
def clean_file(input_file, output_file):
with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8') as outfile:
for line in infile:
# 如果行中不包含需要过滤的关键词，则写入输出文件
if not pattern.search(line):
outfile.write(line)
def analyze_keywords_in_comments(comments_file, keywords_file, output_excel_file):
# 读取评论文件
with open(comments_file, 'r', encoding='utf-8') as file:
comments = file.readlines()
# 读取关键词列表
with open(keywords_file, 'r', encoding='utf-8') as file:
keywords = [line.strip() for line in file]
# 定义一个列表用于存储评论中的 AI 技术应用
ai_technologies = []
# 遍历评论，统计每个关键词的出现次数
for comment in comments:
for keyword in keywords:
if keyword in comment:
ai_technologies.append(keyword)
# 统计每个技术的出现次数
tech_counts = Counter(ai_technologies)
# 将统计结果转换为 DataFrame
df = pd.DataFrame(tech_counts.items(), columns=['AI Technology', 'Count'])
# 将 DataFrame 写入 Excel 文件
df.to_excel(output_excel_file, index=False)
# 排序并提取前 8 名的数据
top_8 = df.sort_values(by='Count', ascending=False).head(8)
# 输出前 8 名的数据
print(top_8)
def generate_wordcloud(text_file, stopwords_file, output_image_file, font_path='msyh.ttc'):
# 加载停用词
def load_stopwords(file_path):
with open(file_path, encoding='utf-8') as f:
stopwords = set(f.read().strip().split('\n'))
return stopwords
# 读取停用词
stopwords = load_stopwords(stopwords_file)
# 读取文本文件
with open(text_file, encoding='utf-8') as f:
txt = f.read()
# 分词并过滤停用词
words = jieba.lcut(txt)
filtered_words = [word for word in words if word not in stopwords]
# 将处理后的词汇拼接成字符串
word_string = ' '.join(filtered_words)
# 生成词云
wc = wordcloud.WordCloud(
width=700,
height=700,
background_color='white',
font_path=font_path
)
wc.generate(word_string)
# 保存词云图
wc.to_file(output_image_file)
urls = ['https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=10&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=324&web_location=1430654&w_rid=420b5e834d7dd54d76f4fba1b7b1e665&wts=1725152144',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=8&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=252&web_location=1430654&w_rid=7fdf1d4b3f7d534c993f50173d02de3f&wts=1725152135',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=7&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=216&web_location=1430654&w_rid=6749123b8b393589cc7c80c1e93ada58&wts=1725152132',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=6&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=180&web_location=1430654&w_rid=74f00cf5195a9ec7ef3d57e347704770&wts=1725152128',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=5&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=144&web_location=1430654&w_rid=e914e50a0da59031c553d631ac5f1fde&wts=1725152124',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=4&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=108&web_location=1430654&w_rid=c622f59f9e1360765b62f0e0bc858fa1&wts=1725152121',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=3&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=72&web_location=1430654&w_rid=a60e99a470fa19919a071c865dd1583f&wts=1725152115',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=2&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=36&web_location=1430654&w_rid=8fc24d10311ce5e5730a84daadbbb6b3&wts=1725152102',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=9&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=288&web_location=1430654&w_rid=a9cbd6c813f6d27561d5f0d583c0ed76&wts=1725153457',
] # 替换为实际的URL
headers = {
'cookie':'buvid4=686CE350-75FA-4921-C069-8D0E582FF02993159-024082507-y91msXDi8JTSAtvVtdhJkQ%3D%3D; buvid3=313C6A34-4C14-0939-EBE8-332F809D2EF655028infoc; b_nut=1725087454; CURRENT_FNVAL=4048; _uuid=10E7EC991-7B18-9A8B-78AA-C95F55102347103610infoc; rpdid=|(JlklRl)~Y|0J\'u~kl|)~l|l; header_theme_version=CLOSE; enable_web_push=DISABLE; is-2022-channel=1; fingerprint=f90b71618c196fb8806f458403d943fb; buvid_fp_plain=undefined; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjU4Njk1NzEsImlhdCI6MTcyNTYxMDMxMSwicGx0IjotMX0.x0CsQ6o6lx4IcK82uHYJjDq_WMedyzoqa081au5YPug; bili_ticket_expires=1725869511; bp_t_offset_1074062089=974427929414991872; buvid_fp=f90b71618c196fb8806f458403d943fb; SESSDATA=e74a05df%2C1741267229%2Ce876a%2A91CjDqLgub8fAVML6ADiSzb56IvMh3z61KnSnawN0g_c1h5emTp3cU9qrpFxgDEzzpawASVkpfc01rblFpaUxDRHViNXpJdGhweEdNY2VDdEJ0N1hvMU92SWdLcG5Dclg5dlZmV29aMWZfX2ZSWHJ5VVN3ZHRkc0ZaLU9COHdmeDR2T0tmSXlvdmt3IIEC; bili_jct=addb604342937a4322aa12322c11bc2c; DedeUserID=3546758143544046; DedeUserID__ckMd5=65316417021aa6ed; sid=7yti0jp9; b_lsid=D810C241D_191CEE2FE76; bsource=search_bing; home_feed_column=5; browser_resolution=1455-699',
'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 Edg/128.0.0.0'
}
def main():
#获取视频aid
process_urls(urls, headers)
print("获取视频aid完毕")
#将视频aid转换成cid
process_aid_and_cid('aid.txt', 'cid.txt', headers)
print("将aid转换成cid完毕，cid去重完成，结果已写回文件。")
#获取视频弹幕
fetch_danmu()
print("弹幕爬取完成。")
# 调用函数进行文件清理
print('开始清洗弹幕')
clean_file('comment.txt', 'cleaned_comment.txt')
print("弹幕清洗完毕")
#数据统计输出
print('开始数据统计')
analyze_keywords_in_comments('cleaned_comment.txt', 'keywords.txt', 'ai_technologies_count.xlsx')
#输出词云图
print("开始构建词云图")
generate_wordcloud('cleaned_comment.txt', 'stopwords.txt', '词云.png')
print("构建词云图完毕")
if __name__ == "__main__":
main()
import requests
import re
import pandas as pd
from collections import Counter
import jieba
import wordcloud
def extract_ids_from_url(url, head, output_file='aid.txt'):
"""
从给定的 URL 中提取 IDs 并将其保存到指定的文件中。
参数:
url (str): 要请求的 URL。
head (dict): 请求头，用于发起 HTTP 请求。
output_file (str): 存储提取的 ID 的文件路径，默认为 'aid.txt'。
"""
try:
# 发起 GET 请求
response = requests.get(url=url, headers=head)
# 确保请求成功，状态码在 200 到 299 之间
response.raise_for_status()
# 将响应内容解析为 JSON 格式
data = response.json()
# 检查响应数据是否包含 'data' 和 'result' 键
if 'data' in data and 'result' in data['data']:
items = data['data']['result']
# 提取每个条目的 'id' 字段
ids = [item['id'] for item in items]
# 以追加模式打开文件，并写入每个 ID
with open(output_file, 'a') as file:
for aid in ids:
file.write(f"{aid}\n")
print(f"IDs have been saved to {output_file}")
else:
print("Unexpected response format") # 如果响应格式不符合预期，输出提示信息
except requests.RequestException as e:
# 捕获并打印请求相关的错误
print(f"Request error: {e}")
except KeyError as e:
# 捕获并打印键错误
print(f"Key error: {e}")
except Exception as e:
# 捕获并打印其他类型的异常
print(f"An error occurred: {e}")
def process_urls(urls1, headers1, output_file='aid.txt'):
"""
遍历 URL 列表，并对每个 URL 调用 extract_ids_from_url 函数进行处理。
参数:
urls1 (list): 包含 URL 的列表。
headers1 (dict): 请求头，用于发起 HTTP 请求。
output_file (str): 存储提取的 ID 的文件路径，默认为 'aid.txt'。
"""
for url in urls1:
extract_ids_from_url(url, headers1, output_file)
def process_aid_and_cid(aid_file_path, cid_file_path, headers):
# 打开 aid 文件，并读取其中的所有 aid
with open(aid_file_path, 'r') as file:
aids = [line.strip() for line in file if line.strip()]
count = 0
# 打开 cid 文件（以追加模式），准备写入 cid 数据
with open(cid_file_path, 'a') as file:
# 遍历每个 aid，构造请求 URL 并获取对应的数据
for aid in aids:
url = f'https://api.bilibili.com/x/player/pagelist?aid={aid}'
response = requests.get(url=url, headers=headers).json()
# 遍历响应数据中的每个条目，提取 cid
for item in response.get('data', []):
cid = item['cid']
# 将 cid 写入文件
file.write(f"{cid}\n")
count += 1
# 输出处理进度
print(f"Processed: {count} CIDs")
def remove_duplicates(file_path):
# 读取 cid 文件中的所有 cid
with open(file_path, 'r') as file:
cids = [line.strip() for line in file if line.strip()]
# 使用字典去除重复的 cid
unique_cids = list(dict.fromkeys(cids))
# 将去重后的 cid 写回文件
with open(file_path, 'w') as file:
for cid in unique_cids:
file.write(cid + '\n')
# 输出去重完成的提示
# 调用 remove_duplicates 函数，去除 cid 文件中的重复项
remove_duplicates(cid_file_path)
def fetch_danmu():
# 读取 cid 文件
print("开始爬取弹幕")
with open('cid.txt', 'r') as file:
cids = [line.strip() for line in file if line.strip()]
for cid in cids:
url = f'https://api.bilibili.com/x/v2/dm/web/history/seg.so?type=1&oid={cid}&date=2024-08-31'
response = requests.get(url=url, headers=headers)
response.encoding = 'utf-8'
# 匹配弹幕内容
content_list = re.findall('[\u4e00-\u9fa5]+', response.text)
content = '\n'.join(content_list)
# 将弹幕写入 comment.txt
with open('comment.txt', mode='a', encoding='utf-8') as f:
f.write(content + '\n')
# 定义需要过滤的关键词或短语
keywords_to_remove = [
'出错啦',
'错误号',
'由于触发哔哩哔哩安全风控策略',
'该次访问请求被拒绝'
]
# 定义一个正则表达式模式，用于匹配需要删除的内容
pattern = re.compile('|'.join(re.escape(keyword) for keyword in keywords_to_remove))
def clean_file(input_file, output_file):
with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8') as outfile:
for line in infile:
# 如果行中不包含需要过滤的关键词，则写入输出文件
if not pattern.search(line):
outfile.write(line)
def analyze_keywords_in_comments(comments_file, keywords_file, output_excel_file):
# 读取评论文件
with open(comments_file, 'r', encoding='utf-8') as file:
comments = file.readlines()
# 读取关键词列表
with open(keywords_file, 'r', encoding='utf-8') as file:
keywords = [line.strip() for line in file]
# 定义一个列表用于存储评论中的 AI 技术应用
ai_technologies = []
# 遍历评论，统计每个关键词的出现次数
for comment in comments:
for keyword in keywords:
if keyword in comment:
ai_technologies.append(keyword)
# 统计每个技术的出现次数
tech_counts = Counter(ai_technologies)
# 将统计结果转换为 DataFrame
df = pd.DataFrame(tech_counts.items(), columns=['AI Technology', 'Count'])
# 将 DataFrame 写入 Excel 文件
df.to_excel(output_excel_file, index=False)
# 排序并提取前 8 名的数据
top_8 = df.sort_values(by='Count', ascending=False).head(8)
# 输出前 8 名的数据
print(top_8)
def generate_wordcloud(text_file, stopwords_file, output_image_file, mask_image_file=None, font_path='msyh.ttc'):
# 加载停用词
def load_stopwords(file_path):
with open(file_path, encoding='utf-8') as f:
stopwords = set(f.read().strip().split('\n'))
return stopwords
# 读取停用词
stopwords = load_stopwords(stopwords_file)
# 读取文本文件
with open(text_file, encoding='utf-8') as f:
txt = f.read()
# 分词并过滤停用词
words = jieba.lcut(txt)
filtered_words = [word for word in words if word not in stopwords]
# 将处理后的词汇拼接成字符串
word_string = ' '.join(filtered_words)
# 设置词云参数
wc_kwargs = {
'width': 700,
'height': 700,
'background_color': 'white',
'font_path': font_path,
}
if mask_image_file:
# 读取掩模图像并确保其为二值图像
mask_image = np.array(Image.open(mask_image_file).convert('L'))
# 反转黑白区域，黑色区域变为0，白色区域变为255
mask = np.where(mask_image == 0, 0, 255).astype(np.uint8)
wc_kwargs.update({
'mask': mask, # 应用掩模图像
'contour_color': 'white', # 轮廓颜色
'contour_width': 0,
})
wc = wordcloud.WordCloud(**wc_kwargs)
wc.generate(word_string)
# 保存词云图
wc.to_file(output_image_file)
urls = ['https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=10&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=324&web_location=1430654&w_rid=420b5e834d7dd54d76f4fba1b7b1e665&wts=1725152144',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=8&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=252&web_location=1430654&w_rid=7fdf1d4b3f7d534c993f50173d02de3f&wts=1725152135',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=7&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=216&web_location=1430654&w_rid=6749123b8b393589cc7c80c1e93ada58&wts=1725152132',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=6&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=180&web_location=1430654&w_rid=74f00cf5195a9ec7ef3d57e347704770&wts=1725152128',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=5&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=144&web_location=1430654&w_rid=e914e50a0da59031c553d631ac5f1fde&wts=1725152124',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=4&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=108&web_location=1430654&w_rid=c622f59f9e1360765b62f0e0bc858fa1&wts=1725152121',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=3&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=72&web_location=1430654&w_rid=a60e99a470fa19919a071c865dd1583f&wts=1725152115',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=2&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=36&web_location=1430654&w_rid=8fc24d10311ce5e5730a84daadbbb6b3&wts=1725152102',
'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page=9&page_size=42&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&qv_id=2vsSEmfb9hpudunbhxw9KMMxggdECjVp&source_tag=3&gaia_vtoken=&dynamic_offset=288&web_location=1430654&w_rid=a9cbd6c813f6d27561d5f0d583c0ed76&wts=1725153457',
] # 替换为实际的URL
headers = {
'cookie':'buvid4=686CE350-75FA-4921-C069-8D0E582FF02993159-024082507-y91msXDi8JTSAtvVtdhJkQ%3D%3D; buvid3=313C6A34-4C14-0939-EBE8-332F809D2EF655028infoc; b_nut=1725087454; CURRENT_FNVAL=4048; _uuid=10E7EC991-7B18-9A8B-78AA-C95F55102347103610infoc; rpdid=|(JlklRl)~Y|0J\'u~kl|)~l|l; header_theme_version=CLOSE; enable_web_push=DISABLE; is-2022-channel=1; fingerprint=f90b71618c196fb8806f458403d943fb; buvid_fp_plain=undefined; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjU4Njk1NzEsImlhdCI6MTcyNTYxMDMxMSwicGx0IjotMX0.x0CsQ6o6lx4IcK82uHYJjDq_WMedyzoqa081au5YPug; bili_ticket_expires=1725869511; bp_t_offset_1074062089=974427929414991872; buvid_fp=f90b71618c196fb8806f458403d943fb; SESSDATA=e74a05df%2C1741267229%2Ce876a%2A91CjDqLgub8fAVML6ADiSzb56IvMh3z61KnSnawN0g_c1h5emTp3cU9qrpFxgDEzzpawASVkpfc01rblFpaUxDRHViNXpJdGhweEdNY2VDdEJ0N1hvMU92SWdLcG5Dclg5dlZmV29aMWZfX2ZSWHJ5VVN3ZHRkc0ZaLU9COHdmeDR2T0tmSXlvdmt3IIEC; bili_jct=addb604342937a4322aa12322c11bc2c; DedeUserID=3546758143544046; DedeUserID__ckMd5=65316417021aa6ed; sid=7yti0jp9; b_lsid=D810C241D_191CEE2FE76; bsource=search_bing; home_feed_column=5; browser_resolution=1455-699',
'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 Edg/128.0.0.0'
}
def main():
#获取视频aid
process_urls(urls, headers)
print("获取视频aid完毕")
#将视频aid转换成cid
process_aid_and_cid('aid.txt', 'cid.txt', headers)
print("将aid转换成cid完毕，cid去重完成，结果已写回文件。")
#获取视频弹幕
fetch_danmu()
print("弹幕爬取完成。")
# 调用函数进行文件清理
print('开始清洗弹幕')
clean_file('comment.txt', 'cleaned_comment.txt')
print("弹幕清洗完毕")
#数据统计输出
print('开始数据统计')
analyze_keywords_in_comments('cleaned_comment.txt', 'keywords.txt', 'ai_technologies_count.xlsx')
#输出词云图
print("开始构建词云图")
generate_wordcloud('cleaned_comment.txt', 'stopwords.txt', '词云.png', 'img.png')
print("构建词云图完毕")
if __name__ == "__main__":
main()

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