48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import pandas as pd
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer # 修改为从transformers导入
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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')
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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(texts):
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"""改进的情感分析函数"""
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# 使用新的模型配置
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model_name = "IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment"
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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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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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() # 假设正例在位置1
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except Exception as e:
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print(f"模型加载失败: {str(e)}")
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return 0.5 # 返回中性评分作为默认值
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if __name__ == "__main__":
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# 示例文件路径
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file_path = "hot_data/GMV/BV1ajXMYUE6S/BV1ajXMYUE6S_273_danmaku.csv"
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# 执行分析
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danmu_texts = load_data(file_path)
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if danmu_texts:
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final_score = analyze_sentiment(danmu_texts)
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print(f"B站弹幕情感评分：{final_score:.4f}")
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else:
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print("未找到有效弹幕数据") |