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105 lines
3.9 KiB
105 lines
3.9 KiB
import requests
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from bs4 import BeautifulSoup
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import time
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import pandas as pd
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from collections import Counter
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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cnt = 0
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# 已爬取视频数
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danmuku_all = []
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# 弹幕库
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headers = {
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"cookie": "cookie",
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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"
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}
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def get_cid(bvid):
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url = f"https://api.bilibili.com/x/player/pagelist?bvid={bvid}"
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try:
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response = requests.get(url, headers=headers, timeout=10)
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response.raise_for_status()
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Json = response.json()
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return Json['data'][0]['cid']
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except requests.exceptions.RequestException as e:
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print(f"请求失败: {e}")
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return None
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def get_danmuku(cid):
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if cid is None:
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return []
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url = f"https://comment.bilibili.com/{cid}.xml"
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try:
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response = requests.get(url, headers=headers, timeout=10)
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response.encoding = 'utf-8'
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soup = BeautifulSoup(response.text, 'xml')
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return [i.text for i in soup.find_all('d')]
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except requests.exceptions.RequestException as e:
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print(f"请求失败: {e}")
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return []
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for Page in range(1, 22): # 1到22页够300个视频
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url = f'https://api.bilibili.com/x/web-interface/search/type?search_type=video&keyword=巴黎奥运会&page={Page}'
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try:
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response = requests.get(url, headers=headers, timeout=10)
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response.raise_for_status()
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Json = response.json()
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results = Json['data']['result']
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for result in results:
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cid = get_cid(result['bvid'])
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danmuku = get_danmuku(cid)
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danmuku_all.extend(danmuku)
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cnt += 1
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if cnt >= 300:
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break
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if cnt >= 300:
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break
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except requests.exceptions.RequestException as e:
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print(f"请求失败: {e}")
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time.sleep(1) # 延时1秒防止被屏蔽
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def filter_danmuku(danmuku_list, keywords):
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# 筛选包含指定关键词的弹幕
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keywords_lower = [keyword.lower() for keyword in keywords] # 关键词小写
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filtered = [d for d in danmuku_list if any(keyword in d.lower() for keyword in keywords_lower)]
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return filtered
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# 读取弹幕文件
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with open('所有视频弹幕.txt', 'r', encoding='utf-8') as file:
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danmuku_all = file.readlines()
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# 筛选包含关键词的弹幕
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keywords = ['AI配音' , 'ai配音' , '人工智能' , 'ai画图' , 'AI画图' , 'AI识曲' , 'AI生成' , '神经网络' , '卷积神经网络' , '循环神经网络' , '智能家居' , '自动驾驶' , '智能推荐' , '智能算法' , '强化学习' , '计算机视觉' , 'ai还原' , 'ai合成']
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filtered_danmuku = filter_danmuku(danmuku_all, keywords)
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# 统计弹幕数量
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counter = Counter(filtered_danmuku)
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most_common = counter.most_common(8)
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# 将结果按列写入Excel
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data = {'弹幕内容': [content.strip() for content, count in most_common],
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'数量': [count for content, count in most_common]}
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df = pd.DataFrame(data)
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df.to_excel('AI_人工智能_弹幕统计.xlsx', index=False)
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print("前8位弹幕统计已保存到 'AI_人工智能_弹幕统计.xlsx'.")
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font_path = r'C:\Windows\Fonts\simhei.ttf'
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try:
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df = pd.read_excel('AI_人工智能_弹幕统计.xlsx')
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if '弹幕内容' not in df.columns:
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raise ValueError("Excel 文件中没有找到 '弹幕内容' 列")
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text = ' '.join(df['弹幕内容'].dropna())
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wordcloud = WordCloud(font_path=font_path, width=800, height=400, background_color='white').generate(text)
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plt.figure(figsize=(10, 5))
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis('off')
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plt.show()
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wordcloud.to_file('词云图.png')
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except FileNotFoundError:
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print("文件未找到，请检查文件路径")
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except ValueError as ve:
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print(ve)
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except Exception as e:
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print(f"发生错误: {e}") |