163 lines
5.4 KiB
Python
163 lines
5.4 KiB
Python
"""
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大模型分析模块 - 调用LLM API分析评论情感
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"""
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import json
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import time
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import re
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from typing import Dict, Optional, Tuple
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from openai import OpenAI, OpenAIError
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class LLMAnalyzer:
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"""大模型情感分析器"""
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SYSTEM_PROMPT = """你是一个专业的情感分析助手。你的任务是分析股吧/论坛评论的情感倾向，判断投资者对该股票的态度。
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评分规则：
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- 0-30: 极度悲观/看空（利空、暴跌、绝望等情绪）
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- 31-50: 偏悲观/中性（担忧、谨慎、观望等情绪）
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- 51-70: 偏乐观/中性（看好、希望、期待等情绪）
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- 71-100: 极度乐观/看涨（利好、暴涨、兴奋等情绪）
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请直接输出一个JSON格式的结果，包含两个字段：
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- score: 0-100的整数评分
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- label: 简短的态度描述（如"强烈看跌"、"谨慎观望"、"温和看涨"、"强烈看涨"等）
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注意：
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1. 只返回JSON，不要有其他文字
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2. 如果无法判断，返回50和"无法判断"
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3. 分析要客观，不要被表面文字迷惑
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"""
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def __init__(self, config: Dict):
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self.config = config
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self.base_url = config.get('base_url', 'https://api.openai.com/v1')
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self.api_key = config.get('api_key', '')
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self.model = config.get('model', 'gpt-3.5-turbo')
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self.timeout = config.get('timeout', 30)
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self.retry_times = config.get('retry_times', 3)
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self.client = None
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if self.api_key:
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self._init_client()
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def _init_client(self):
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"""初始化OpenAI客户端"""
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try:
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url,
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timeout=self.timeout
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)
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except Exception as e:
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print(f"初始化LLM客户端失败: {e}")
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def update_config(self, config: Dict):
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"""更新配置"""
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self.config.update(config)
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self.base_url = config.get('base_url', self.base_url)
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self.api_key = config.get('api_key', self.api_key)
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self.model = config.get('model', self.model)
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self.timeout = config.get('timeout', self.timeout)
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self.retry_times = config.get('retry_times', self.retry_times)
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if self.api_key:
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self._init_client()
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def analyze(self, comment: str) -> Tuple[Optional[int], Optional[str]]:
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"""
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分析单条评论
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返回 (score, label)
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"""
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if not self.client:
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return None, "LLM未配置"
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if not comment or not comment.strip():
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return None, "评论为空"
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for attempt in range(self.retry_times):
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": self.SYSTEM_PROMPT},
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{"role": "user", "content": f"请分析以下评论的情感倾向：\n\n{comment}"}
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],
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temperature=0.3,
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max_tokens=200
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)
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result_text = response.choices[0].message.content.strip()
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score, label = self._parse_response(result_text)
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if score is not None:
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return score, label
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except OpenAIError as e:
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print(f"API调用失败 (尝试 {attempt + 1}/{self.retry_times}): {e}")
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if attempt < self.retry_times - 1:
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time.sleep(2 ** attempt) # 指数退避
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except Exception as e:
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print(f"分析过程出错: {e}")
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break
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return None, "分析失败"
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def _parse_response(self, response: str) -> Tuple[Optional[int], Optional[str]]:
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"""解析LLM返回的结果"""
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try:
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# 尝试直接解析JSON
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result = json.loads(response)
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score = result.get('score', 50)
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label = result.get('label', '无法判断')
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# 验证分数范围
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score = max(0, min(100, int(score)))
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return score, label
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except json.JSONDecodeError:
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# 尝试从文本中提取
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pass
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# 尝试从文本中提取数字
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numbers = re.findall(r'\b(\d{1,3})\b', response)
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if numbers:
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score = int(numbers[0])
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score = max(0, min(100, score))
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# 提取标签
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label_match = re.search(r'["']([^"']+)["']', response)
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if label_match:
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label = label_match.group(1)
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else:
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label = response.split('\n')[0][:20] if response else '无法判断'
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return score, label
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return None, "解析失败"
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def analyze_batch(self, comments: list, delay: float = 1.0) -> list:
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"""
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批量分析评论
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delay: 每次调用之间的延迟（秒）
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"""
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results = []
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for i, comment in enumerate(comments):
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print(f"分析评论 {i + 1}/{len(comments)}...")
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score, label = self.analyze(comment)
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results.append({
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'content': comment,
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'score': score,
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'label': label
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})
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if delay > 0 and i < len(comments) - 1:
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time.sleep(delay)
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return results
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def is_configured(self) -> bool:
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"""检查是否已配置"""
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return bool(self.client and self.api_key) |