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156 lines
6.1 KiB
156 lines
6.1 KiB
import requests
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import re
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import time
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import pandas as pd
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import matplotlib.pyplot as plt
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from wordcloud import WordCloud
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from collections import Counter
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headers = {
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'cookie':'CURRENT_FNVAL=4048; buvid4=CDB22228-76EA-BC93-F037-78FC6CEC077D36275-023090719-X83v1qigvaVs%2BeTu3%2F5T2g%3D%3D; rpdid=|(u)luk)m|)R0J\'uYmR|)Y)J); enable_web_push=DISABLE; header_theme_version=CLOSE; DedeUserID=567151924; DedeUserID__ckMd5=3d51b3cb3879b2e0; PVID=1; buvid3=909EA327-0037-5349-5CF4-5B2C4EF5300103254infoc; b_nut=1726374703; bsource=search_bing; _uuid=A28E1582-10535-12B8-49B2-D1F1026722D1607876infoc; buvid_fp=1ed033837a881c0f6fee6ce1ae293ed0; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY2MzM5MDQsImlhdCI6MTcyNjM3NDY0NCwicGx0IjotMX0.52ZE1RDG5tSqHh-ZOGgEHDzj6W1UyOtiMkcxw_a2WNY; bili_ticket_expires=1726633844; SESSDATA=701189c6%2C1741926705%2C58e19%2A91CjCANFin8L-nRK6CjxH9_BSgRe6HHUSWybFilZklu8yORRObfCV2cnJswJPECKKy1UcSVkMtcE4ydHItZF9lOW43ZFpyelRVVEUzZUVCdlh6S2ltWWJIaTg0MU1DclRIeG8wbE84cE1pSFBkOXA1alNxTkp3bDJuLWNCN2IzV2JXX2p4SGIxaW9BIIEC; bili_jct=98cbd1d0535939dc4a5c474a44d27ad7; sid=7wfo5xb3; home_feed_column=5; browser_resolution=1432-776; CURRENT_QUALITY=80; bp_t_offset_567151924=977713175669506048; b_lsid=811052DA4_191FB79926A',
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'Referer': 'https://search.bilibili.com/all?vt=82484714&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&from_source=webtop_search&spm_id_from=333.1007&search_source=5',
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'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'
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}
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def get_response(url):
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response = requests.get(url = url, headers = headers)
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return response
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def get_bv(page_num):
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bv_list = []
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link = 'https://api.bilibili.com/x/web-interface/wbi/search/type'
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data = {
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'category_id':'',
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'search_type': 'video',
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'ad_resource': '5654',
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'__refresh__': 'true',
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'_extra':'',
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'context':'',
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'page': page_num,
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'page_size': '42',
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'pubtime_begin_s': '0',
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'pubtime_end_s': '0',
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'from_source':'',
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'from_spmid': '333.337',
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'platform': 'pc',
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'highlight': '1',
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'single_column': '0',
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'keyword': '2024巴黎奥运会',
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'qv_id': 'Hz50pRYmKFQYlX2AorY3bJUTNJbLRwnX',
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'source_tag': '3',
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'gaia_vtoken':'',
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'dynamic_offset': '30',
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'web_location': '1430654',
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'w_rid': '1b994979977a17ee8010f012d43fa7b6',
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'wts': '1726501056'
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}
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link_data = requests.get(url = link, headers = headers, params = data).json()
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for index in link_data['data']['result']:
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bv_list.append(index['bvid'])
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# pprint(bv_list)
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return bv_list
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def get_cid(bvid):
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url = f"https://api.bilibili.com/x/web-interface/view?bvid={bvid}"
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data = get_response(url).json()
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# pprint(data)
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return data['data']['cid']
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def get_danmaku(cid):
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url = f"https://comment.bilibili.com/{cid}.xml"
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response = get_response(url)
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response.encoding = 'utf-8'
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# pprint(response.text)
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return response.text
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def parse_danmaku(danmaku_xml):
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danmaku_list = re.findall('">(.*?)</d>',danmaku_xml)
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return danmaku_list
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# 弹幕相关关键词
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ai_keywords = ["AI", "人工智能", "机器学习", "深度学习", "AI技术", "自动驾驶", "智能", "图像识别", "AI应用","智造"]
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# 筛选与AI相关的弹幕
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def get_ai_danmaku(danmaku_list_all, ai_keywords):
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ai_related_danmaku = [danmaku for danmaku in danmaku_list_all if any(keyword in danmaku for keyword in ai_keywords)]
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return ai_related_danmaku
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# 统计弹幕出现频次
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def count_danmaku(danmaku_list):
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danmaku_counter = Counter(danmaku_list)
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return danmaku_counter
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# 获取前n个弹幕及其出现次数
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def get_top_n_danmaku(danmaku_counter, n=8):
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return danmaku_counter.most_common(n)
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# 写入 Excel
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def write_to_excel(data, filename='danmaku_AI_top8.xlsx'):
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df = pd.DataFrame(data, columns=['弹幕内容', '出现次数'])
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df.to_excel(filename, index=False)
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# 生成并显示词云图
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def get_wordcloud(danmaku_counter):
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wordcloud = WordCloud(
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width=800, # 宽度
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height=400, # 高度
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background_color='white', # 背景色
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max_words=100, # 显示的最大词语数量
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colormap='viridis', # 颜色映射
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font_path='msyh.ttc' # 指定字体路径，适应中文显示
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).generate_from_frequencies(danmaku_counter)
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plt.figure(figsize=(10, 5)) # 图像大小
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plt.imshow(wordcloud, interpolation="bilinear")
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plt.axis("off") # 关闭坐标轴显示
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plt.show()
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if __name__ == '__main__':
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# 收集前300个视频的bvid
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videos = []
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page_num = 1
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while len(videos) < 300:
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bv_list = get_bv(str(page_num))
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videos.extend(bv_list)
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page_num += 1
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time.sleep(1) # 防止请求过于频繁
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# 仅保留前300个视频号
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videos = videos[:300]
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all_danmaku = []
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all_ai_related_danmaku = []
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# 获取每个视频的弹幕并打印
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for bvid in videos:
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try:
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cid = get_cid(bvid)
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danmaku_xml = get_danmaku(cid)
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danmaku_list = parse_danmaku(danmaku_xml)
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# 筛选AI相关弹幕
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ai_related_danmaku = get_ai_danmaku(danmaku_list, ai_keywords)
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all_ai_related_danmaku.extend(ai_related_danmaku)
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for danmaku in danmaku_list:
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all_danmaku.append(danmaku)
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except Exception as e:
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print(f"Error fetching danmaku for video {bvid}: {e}")
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time.sleep(1) # 防止请求过于频繁
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# 统计ai弹幕出现频次
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ai_danmaku_counter = count_danmaku(all_ai_related_danmaku)
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# 获取前8个频次最高的弹幕
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top_8_danmaku = get_top_n_danmaku(ai_danmaku_counter, n=8)
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for dm,cnt in top_8_danmaku:
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print(dm)
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# 输出到Excel
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write_to_excel(top_8_danmaku)
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# 生成词云
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get_wordcloud(ai_danmaku_counter)
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# print("弹幕统计结果已保存到Excel: danmaku_AI_top8.xlsx")
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