74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
import pandas as pd
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import numpy as np
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from snownlp import SnowNLP
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import os
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def load_data(file_path):
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try:
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df = pd.read_csv(file_path, usecols=['弹幕内容'], engine='python')
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return df['弹幕内容'].dropna().astype(str).tolist()
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except Exception as e:
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print(f"数据加载失败: {str(e)}")
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return []
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def analyze_sentiment(danmu_texts):
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sentiment_scores = []
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for item in danmu_texts:
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s=SnowNLP(item)
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sentiment_scores.append(s.sentiments)
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avg_score = np.mean(sentiment_scores)
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return avg_score
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def process_all_partitions(base_path):
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# 获取所有分区目录
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partitions = [d for d in os.listdir(base_path)
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if os.path.isdir(os.path.join(base_path, d))]
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for partition in partitions:
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partition_path = os.path.join(base_path, partition)
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print(f"正在处理分区: {partition}")
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process_partition(partition_path)
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# process_partition函数
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def process_partition(partition_path):
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info_file = os.path.join(partition_path, 'info.csv')
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if not os.path.exists(info_file):
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print(f"未找到info文件: {info_file}")
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return
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info_df = pd.read_csv(info_file, encoding='utf-8')
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# 创建与info_df行数相同的空列表，初始值为None
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scores = [None] * len(info_df)
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for idx, bv in enumerate(info_df['BV号']):
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# 构建弹幕文件目录路径
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danmu_dir = os.path.join(partition_path, bv)
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if not os.path.exists(danmu_dir):
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continue # 保持None值
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# 查找匹配的弹幕文件
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danmu_files = [f for f in os.listdir(danmu_dir)
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if f.startswith(bv) and f.endswith('danmaku.csv')]
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if not danmu_files:
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continue # 保持None值
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danmu_file = os.path.join(danmu_dir, danmu_files[0])
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danmu_texts = load_data(danmu_file)
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if not danmu_texts:
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continue # 保持None值
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# 将结果放入对应的索引位置
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scores[idx] = analyze_sentiment(danmu_texts)
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info_df['弹幕情感评分snowNLP'] = scores
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info_df.to_csv(info_file, index=False, encoding='utf-8-sig')
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# 使用示例 - 处理所有分区
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process_all_partitions("hot_data")
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process_all_partitions("nohot_data") |