From 83cfe3156b31a04e31143c34cb9a84f42f406753 Mon Sep 17 00:00:00 2001
From: psljvnofc <477590211@qq.com>
Date: Wed, 18 Sep 2024 20:52:17 +0800
Subject: [PATCH] =?UTF-8?q?=E7=AD=9B=E9=80=89=E5=BC=B9=E5=B9=95=E5=86=85?=
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---
 shaixdanmu.py | 41 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 shaixdanmu.py

diff --git a/shaixdanmu.py b/shaixdanmu.py
new file mode 100644
index 0000000..3b8f6d4
--- /dev/null
+++ b/shaixdanmu.py
@@ -0,0 +1,41 @@
+from collections import Counter
+import pandas as pd
+
+# 读取xlsx文件
+df = pd.read_excel('danmaku_content.xlsx',engine='openpyxl')
+
+# 假设弹幕数据在名为'danmaku'的列中
+danmaku_column = df['content']
+
+def filter_ai_danmaku(danmaku_series, keywords):
+    ai_danmaku = []
+    for danmaku in danmaku_series:
+        if pd.notna(danmaku):  # 检查弹幕是否为空
+            danmaku_str = str(danmaku)  # 转换为字符串
+            if any(keyword in danmaku_str for keyword in keywords):
+                ai_danmaku.append(danmaku_str)
+    return ai_danmaku
+
+ai_keywords = ["AI", "AI技术", "机器学习", "深度学习", "智能", "VR/AR", "全景直播", "360度", "3D", "追踪" ,"虚拟", "数字", "人工智能", "面部识别", "云技术", "安保", "检测", "监测", "福州大学"]
+# 筛选与AI相关的弹幕
+filtered_danmaku = filter_ai_danmaku(danmaku_column, ai_keywords)
+
+# print(len(filtered_danmaku))
+
+# 统计每种弹幕的数量
+danmaku_count = Counter(filtered_danmaku)
+
+# 排序并获取前n项
+sorted_danmaku = danmaku_count.most_common(15)
+
+# print(sorted_danmaku)
+
+# 输出排名前n的弹幕及数量
+for i, (danmaku, count) in enumerate(sorted_danmaku, 1):
+    print(f"排名 {i}: {danmaku} - 数量: {count}")
+
+# 将数据转化为DataFrame
+danmaku_df = pd.DataFrame(sorted_danmaku, columns=['弹幕内容', '数量'])
+
+# 导出到Excel文件
+danmaku_df.to_excel('ai_danmaku_statistics.xlsx', index=False, engine='openpyxl')
\ No newline at end of file