86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# 修改导入部分
|
||
from transformers import AutoModelForSequenceClassification, AutoTokenizer # 替换为 transformers 库
|
||
import pandas as pd
|
||
import torch
|
||
import os
|
||
|
||
def load_data(file_path):
|
||
"""优化后的数据加载函数"""
|
||
try:
|
||
df = pd.read_csv(file_path, usecols=['弹幕内容'], engine='python')
|
||
return df['弹幕内容'].dropna().astype(str).tolist()
|
||
except Exception as e:
|
||
print(f"数据加载失败: {str(e)}")
|
||
return []
|
||
|
||
|
||
def analyze_sentiment(texts):
|
||
"""改进的情感分析函数"""
|
||
try:
|
||
# 使用 HuggingFace 的模型
|
||
model_name = "IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment"
|
||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||
model = AutoModelForSequenceClassification.from_pretrained(model_name)
|
||
|
||
# 批量处理提升效率
|
||
inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
|
||
with torch.no_grad():
|
||
outputs = model(**inputs)
|
||
|
||
# 调整概率计算方式
|
||
probs = torch.softmax(outputs.logits, dim=1)
|
||
return probs[:, 1].mean().item()
|
||
|
||
except Exception as e:
|
||
print(f"情感分析失败: {str(e)}")
|
||
return 0.5 # 错误时返回中性值
|
||
|
||
def process_all_partitions(base_path):
|
||
# 获取所有分区目录
|
||
partitions = [d for d in os.listdir(base_path)
|
||
if os.path.isdir(os.path.join(base_path, d))]
|
||
|
||
for partition in partitions:
|
||
partition_path = os.path.join(base_path, partition)
|
||
print(f"正在处理分区: {partition}")
|
||
process_partition(partition_path)
|
||
|
||
# process_partition函数
|
||
def process_partition(partition_path):
|
||
info_file = os.path.join(partition_path, 'info.csv')
|
||
if not os.path.exists(info_file):
|
||
print(f"未找到info文件: {info_file}")
|
||
return
|
||
|
||
info_df = pd.read_csv(info_file, encoding='utf-8')
|
||
# 创建与info_df行数相同的空列表，初始值为None
|
||
scores = [None] * len(info_df)
|
||
|
||
for idx, bv in enumerate(info_df['BV号']):
|
||
# 构建弹幕文件目录路径
|
||
danmu_dir = os.path.join(partition_path, bv)
|
||
if not os.path.exists(danmu_dir):
|
||
continue # 保持None值
|
||
|
||
# 查找匹配的弹幕文件
|
||
danmu_files = [f for f in os.listdir(danmu_dir)
|
||
if f.startswith(bv) and f.endswith('danmaku.csv')]
|
||
|
||
if not danmu_files:
|
||
continue # 保持None值
|
||
|
||
danmu_file = os.path.join(danmu_dir, danmu_files[0])
|
||
danmu_texts = load_data(danmu_file)
|
||
|
||
if not danmu_texts:
|
||
continue # 保持None值
|
||
|
||
# 将结果放入对应的索引位置
|
||
scores[idx] = analyze_sentiment(danmu_texts)
|
||
|
||
info_df['弹幕情感评分RoBERTa'] = scores
|
||
info_df.to_csv(info_file, index=False, encoding='utf-8-sig')
|
||
|
||
# 使用示例 - 处理所有分区
|
||
process_all_partitions("hot_data")
|
||
process_all_partitions("nohot_data") |