戒酒的李白
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
from transformers import pipeline
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import re
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def preprocess_text(text):
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"""简单的文本预处理"""
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text = re.sub(r"\{%.+?%\}", " ", text) # 去除 {%xxx%}
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text = re.sub(r"@.+?( |$)", " ", text) # 去除 @xxx
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text = re.sub(r"【.+?】", " ", text) # 去除 【xx】
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text = re.sub(r"\u200b", " ", text) # 去除特殊字符
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text = re.sub(r"\s+", " ", text) # 多个空格合并
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return text.strip()
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def main():
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print("正在加载微博情感分析模型...")
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# 使用pipeline方式 - 更简单
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model_name = "wsqstar/GISchat-weibo-100k-fine-tuned-bert"
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try:
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classifier = pipeline(
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"text-classification",
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model=model_name,
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return_all_scores=True
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)
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print("模型加载成功!")
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except Exception as e:
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print(f"模型加载失败: {e}")
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print("请检查网络连接")
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return
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print("\n============= 微博情感分析 (Pipeline版) =============")
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print("输入微博内容进行分析 (输入 'q' 退出):")
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while True:
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text = input("\n请输入微博内容: ")
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if text.lower() == 'q':
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break
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if not text.strip():
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print("输入不能为空，请重新输入")
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continue
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try:
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# 预处理文本
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processed_text = preprocess_text(text)
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# 预测
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outputs = classifier(processed_text)
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# 解析结果
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positive_score = None
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negative_score = None
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for output in outputs[0]:
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if output['label'] == 'LABEL_1': # 正面
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positive_score = output['score']
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elif output['label'] == 'LABEL_0': # 负面
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negative_score = output['score']
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# 确定预测结果
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if positive_score > negative_score:
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label = "正面情感"
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confidence = positive_score
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else:
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label = "负面情感"
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confidence = negative_score
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print(f"预测结果: {label} (置信度: {confidence:.4f})")
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except Exception as e:
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print(f"预测时发生错误: {e}")
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continue
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if __name__ == "__main__":
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main() |