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MyBullet.py

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+import re
+import requests
+from bs4 import BeautifulSoup
+from collections import Counter
+import pandas as pd
+
+# 定义需要爬取的视频数量和搜索内容
+BV_NUM = 300  # 需要获取的视频数量
+SEARCH_CONTENT = "2024巴黎奥运会"  # 搜索关键词
+
+# 请求头，防止反爬
+HEADERS = {
+    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0',
+    "Referer": "https://search.bilibili.com/all?"
+}
+
+# 根据搜索关键词，爬取指定数量的视频BVID
+def get_bv(num):
+    """
+    参数： num: 需要获取的BVID数量
+    返回值： bv_list, 一个包含视频BVID的集合
+    """
+    bv_list = set()  # 使用集合存储获取的BVID，以去重
+    page = 1  # 初始化页码
+    
+    while len(bv_list) < num:
+        # 构造搜索页面的URL
+        search_url = f"https://search.bilibili.com/all?keyword={SEARCH_CONTENT}&page={page}"
+        response = requests.get(search_url, headers=HEADERS)
+        
+        # 使用正则表达式提取BVID
+        pattern = re.compile(r'aid:.*?bvid:"(?P<bvs>.*?)",')
+        matches = pattern.finditer(response.text)
+        
+        # 将BVID加入集合
+        for match in matches:
+            bv_list.add(match.group("bvs"))
+            # 如果达到了指定数量，直接返回结果
+            if len(bv_list) >= num:
+                return bv_list
+        
+        # 增加页码，继续爬取下一页
+        page += 1
+    
+    return bv_list
+
+# 通过bv号获取视频cid，进一步获取弹幕内容
+def fetch_bullet_screen(bv_list):
+    """
+    参数 bv_list: 包含BV号的列表
+    返回值: 弹幕内容列表
+    """
+    my_bullet = []  # 存放所有弹幕的列表
+    for bv in bv_list:
+        cid_url = f"https://api.bilibili.com/x/player/pagelist?bvid={bv}&jsonp=jsonp"
+        response_cid = requests.get(cid_url, headers=HEADERS)
+        response_cid_json = response_cid.json()
+        
+        # 获取视频的cid
+        cid = response_cid_json.get("data", [{}])[0].get("cid")
+        if not cid:
+            print(f"无法获取 {bv} 的cid")
+            continue
+        
+        # 获取对应cid的弹幕内容
+        response_bullet = requests.get(f"https://comment.bilibili.com/{cid}.xml", headers=HEADERS)
+        response_bullet.encoding = "utf-8"
+        
+        # 解析XML格式的弹幕数据
+        soup = BeautifulSoup(response_bullet.text, "xml")
+        danmus = soup.find_all("d")
+        
+        # 将弹幕内容添加到列表中
+        my_bullet.extend([danmu.text for danmu in danmus])
+        print(f"已成功爬取 {bv} 的弹幕")
+    
+    return my_bullet
+
+# 分析弹幕内容，提取包含AI相关词语的完整句子
+def analyze_bullet_screen_with_ai_sentences(my_bullet , ai_keywords):
+    """
+    参数1： my_bullet: 弹幕内容列表
+    参数2： ai_keywords:关键词
+    返回值: 包含AI关键词的完整句子列表
+    返回值: 包含AI关键词的列表
+    返回值: 关键词统计
+    """
+    
+    ai_sentences = []  # 包含关键词的完整句子
+    keyword_only_sentences = []  # 只包含关键词的句子
+    keyword_counts = Counter()  # 计数器，用于统计每个关键词的频次
+
+    for bullet in my_bullet:
+        contains_keyword = False
+        for keyword in ai_keywords:
+            if keyword in bullet:
+                contains_keyword = True
+                ai_sentences.append(bullet)
+                keyword_only_sentences.append(keyword)
+                keyword_counts[keyword] += 1  # 更新关键词计数
+            
+        if contains_keyword:
+            # 只保留包含的关键词
+            keywords_in_bullet = [keyword for keyword in ai_keywords if keyword in bullet]
+    
+    return ai_sentences, keyword_only_sentences, keyword_counts
+
+# 将关键词统计信息保存到Excel文件
+def save_keyword_counts_to_excel(keyword_counts, path='keyword_counts.xlsx'):
+    """
+    参数1: keyword_counts: Counter对象，包含每个关键词及其出现次数
+    参数2: path: Excel文件名
+    """
+    counts_df = pd.DataFrame(keyword_counts.items(), columns=["关键词", "出现次数"])
+    
+    # 按出现次数排序，从高到低
+    counts_df.sort_values(by='出现次数', ascending=False, inplace=True)
+    
+    counts_df.to_excel(path, index=False)
+    print(f"关键词统计已保存到 {path}")
+
+# 将包含AI关键词的句子保存到Excel文件
+def save_sentences_to_excel(ai_sentences, keyword_only_sentences,path1='ai_sentences.xlsx',path2='keyword_only_sentences.xlsx',choose1=True ,choose2=True):
+    """
+    参数1: ai_sentences: 包含AI关键词的句子列表
+    参数2: keyword_only_sentences: 只包含AI关键字的列表
+    参数3: path1: Excel文件名,保存AI关键词句子列表
+    参数4: path2: Excel文件名,保存AI关键词
+    参数5: choose1: 是否保存path1
+    参数6: choose2: 是否保存path2
+    """
+    
+    if(choose1):
+        ai_df = pd.DataFrame(ai_sentences, columns=["包含关键词的弹幕"])
+        ai_df.to_excel(path1, index=False)
+    if(choose2):
+        keyword_only_df = pd.DataFrame(keyword_only_sentences, columns=["关键词"])
+        keyword_only_df.to_excel(path2, index=False)
+    
+    print(f"包含关键词的弹幕句子已保存到 {path1}")
+    print(f"只包含关键词的句子已保存到 {path2}")
+
+def main_bullet():
+    bv_list = get_bv(BV_NUM)
+    print(bv_list)
+    
+    bullet_screens = fetch_bullet_screen(bv_list)
+    print(f"获取了 {len(bullet_screens)} 条弹幕")
+    
+    ai_keywords = [
+        "AI", "人工智能", "机器学习", "深度学习", "神经网络", "算法", "智能", "大数据", "自动化", "机器人",
+        "计算机视觉", "自然语言处理", "NLP", "语音识别", "自动驾驶", "边缘计算", "强化学习", 
+        "生成对抗网络", "GAN", "迁移学习", "数据挖掘", "语义分析", "图像识别", "深度神经网络", "DNN",
+        "决策树", "随机森林", "集成学习", "模糊逻辑", "专家系统", "计算智能", "大规模并行处理", 
+        "分布式系统", "物联网", "IoT", "云计算", "区块链", "量子计算", "图神经网络", "GNN",
+        "人机交互", "HCI", "情感分析", "机器人过程自动化", "RPA", "无人机", "UAV", "智能城市", 
+        "云原生", "分布式学习", "元学习", "数字孪生", "自动化运维", "AIOps"
+    ]
+    
+    ai_sentences, keyword_only_sentences, keyword_counts = analyze_bullet_screen_with_ai_sentences(bullet_screens, ai_keywords)
+    print(f"提取了 {len(ai_sentences)} 条包含AI相关关键词的弹幕")
+    
+    save_sentences_to_excel(ai_sentences, keyword_only_sentences, choose2 = False)
+    save_keyword_counts_to_excel(keyword_counts)
+
+if __name__ == '__main__':
+    main_bullet()