111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
import os
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# 设置HuggingFace国内镜像源（添加在文件最开头）
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os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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# 修改导入部分
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from transformers import AutoModelForSequenceClassification, AutoTokenizer # 替换为 transformers 库
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import pandas as pd
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import torch
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import os
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# 在文件开头添加导入
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from tqdm import tqdm
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def load_data(file_path):
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"""优化后的数据加载函数"""
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try:
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df = pd.read_csv(file_path, usecols=['弹幕内容'], engine='python', encoding='utf-8')
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print(f"调试信息 - 文件 {file_path} 包含的列名: {list(df.columns)}")
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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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# 在analyze_sentiment函数中添加模型路径处理
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def analyze_sentiment(texts):
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"""改进的情感分析函数"""
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try:
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# 如果弹幕数量超过500，均匀抽样
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if len(texts) > 500:
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step = len(texts) // 500
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texts = texts[::step][:500]
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# 使用 HuggingFace 的模型
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model_path = "IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForSequenceClassification.from_pretrained(model_path)
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# 将模型移动到GPU
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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# 批量处理提升效率
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inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
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inputs = {k: v.to(device) for k, v in inputs.items()} # 将输入数据也移动到GPU
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with torch.no_grad():
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outputs = model(**inputs)
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# 调整概率计算方式
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probs = torch.softmax(outputs.logits, dim=1)
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return probs[:, 1].mean().item()
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except Exception as e:
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print(f"情感分析失败: {str(e)}")
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return 0.5 # 错误时返回中性值
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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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scores = [None] * len(info_df)
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# 添加进度条
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with tqdm(total=len(info_df), desc=f"处理分区 {os.path.basename(partition_path)}") as pbar:
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for idx, bv in enumerate(info_df['BV号']):
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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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pbar.update(1)
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continue
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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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pbar.update(1)
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continue
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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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pbar.update(1)
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continue
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scores[idx] = analyze_sentiment(danmu_texts)
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pbar.update(1)
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pbar.set_postfix({'当前BV号': bv, '评分': scores[idx]})
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info_df['弹幕情感评分RoBERTa'] = 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")
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while True :
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pass
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