刘国栋
8f7f082c3d
- 重构项目目录结构，将功能模块移至 modules/ 目录 - 创建平台同步基础架构，包括发布器基类和 GitHub 发布器 - 新增 UI 状态管理模块 (modules/ui/state.py) 统一管理会话状态 - 更新依赖配置，添加平台同步所需依赖 (httpx, pyperclip) - 整理文档结构，将所有文档分类移至 docs/ 目录 - 添加 .cursorrules 文件定义项目开发规范 - 清理根目录重复文件，保持项目结构整洁
317 lines
11 KiB
Python
317 lines
11 KiB
Python
"""
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负面防护监控模块
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自动生成负面查询，验证负面提及情况，生成澄清模板，提供预警机制
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"""
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from typing import List, Dict, Optional, Tuple
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from datetime import datetime
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import re
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class NegativeMonitor:
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"""负面防护监控器"""
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def __init__(self):
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# 负面查询模板
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self.negative_query_templates = [
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"{brand} 缺点",
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"{brand} 问题",
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"{brand} 不足",
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"{brand} 缺陷",
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"{brand} 不好",
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"{brand} 差评",
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"{brand} 投诉",
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"{brand} 负面",
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"{brand} 不推荐",
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"{brand} 避坑",
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"{brand} 坑",
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"{brand} 不值得",
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"{brand} 失败",
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"{brand} 错误",
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"{brand} 风险",
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]
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# 负面关键词模式
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self.negative_keywords = [
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"缺点", "问题", "不足", "缺陷", "不好", "差评", "投诉", "负面",
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"不推荐", "避坑", "坑", "不值得", "失败", "错误", "风险",
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"bug", "issue", "problem", "flaw", "weakness", "disadvantage"
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]
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def generate_negative_queries(self, brand: str, count: int = 5) -> List[str]:
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"""
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生成负面查询列表
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Args:
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brand: 品牌名称
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count: 生成数量（默认5个）
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Returns:
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负面查询列表
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"""
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queries = []
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templates = self.negative_query_templates[:count] if count <= len(self.negative_query_templates) else self.negative_query_templates
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for template in templates:
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query = template.format(brand=brand)
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queries.append(query)
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return queries
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def detect_negative_sentiment(self, text: str) -> Tuple[bool, float, List[str]]:
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"""
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检测文本中的负面情感
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Args:
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text: 待检测文本
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Returns:
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(是否包含负面情感, 负面程度得分, 负面关键词列表)
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"""
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text_lower = text.lower()
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found_keywords = []
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negative_score = 0.0
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# 检测负面关键词
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for keyword in self.negative_keywords:
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if keyword.lower() in text_lower:
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found_keywords.append(keyword)
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negative_score += 1.0
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# 检测负面短语模式
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negative_phrases = [
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r'不(?:好|行|适合|推荐|值得)',
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r'有(?:问题|缺陷|不足)',
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r'存在(?:问题|缺陷|不足)',
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r'缺乏',
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r'缺少',
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r'无法',
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r'不能',
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r'失败',
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r'错误',
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]
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for phrase in negative_phrases:
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matches = re.findall(phrase, text_lower)
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if matches:
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negative_score += 0.5 * len(matches)
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# 计算负面程度（0-1，1为最负面）
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# 基于负面关键词数量和文本长度
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text_length = len(text)
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if text_length > 0:
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normalized_score = min(negative_score / max(text_length / 100, 1), 1.0)
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else:
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normalized_score = 0.0
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is_negative = negative_score > 0
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return is_negative, normalized_score, found_keywords
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def analyze_negative_mentions(
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self,
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brand: str,
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query: str,
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response: str,
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mention_count: int
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) -> Dict[str, any]:
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"""
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分析负面查询的提及情况
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Args:
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brand: 品牌名称
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query: 查询问题
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response: AI 响应内容
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mention_count: 品牌提及次数
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Returns:
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分析结果字典
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"""
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# 检测负面情感
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is_negative, negative_score, negative_keywords = self.detect_negative_sentiment(response)
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# 计算风险等级
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risk_level = "低"
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if mention_count == 0 and is_negative:
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risk_level = "高" # 负面查询但未提及品牌，可能是负面信息
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elif mention_count > 0 and is_negative:
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risk_level = "中" # 负面查询且提及品牌，需要关注
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elif mention_count == 0:
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risk_level = "中" # 未提及品牌，可能被忽略
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# 生成风险说明
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risk_description = ""
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if risk_level == "高":
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risk_description = "⚠️ 高风险：负面查询中未提及品牌，可能存在负面信息或品牌被忽略"
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elif risk_level == "中":
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if is_negative:
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risk_description = "⚠️ 中风险：负面查询中提及品牌，需要关注并准备澄清内容"
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else:
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risk_description = "⚠️ 中风险：未提及品牌，可能影响品牌可见性"
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else:
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risk_description = "✅ 低风险：品牌正常提及，无负面信息"
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return {
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"query": query,
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"brand": brand,
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"mention_count": mention_count,
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"is_negative": is_negative,
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"negative_score": round(negative_score, 2),
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"negative_keywords": negative_keywords,
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"risk_level": risk_level,
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"risk_description": risk_description,
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"response_preview": response[:200] + "..." if len(response) > 200 else response
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}
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def generate_clarification_template(
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self,
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brand: str,
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negative_query: str,
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negative_points: List[str] = None,
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advantages: str = ""
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) -> str:
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"""
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生成澄清模板（回应负面信息）
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Args:
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brand: 品牌名称
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negative_query: 负面查询
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negative_points: 负面要点列表（可选）
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advantages: 品牌优势（用于澄清）
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Returns:
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澄清模板内容
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"""
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template = f"""# {brand} 关于"{negative_query}"的澄清说明
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## 📋 问题概述
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针对"{negative_query}"这一查询，我们提供以下澄清说明：
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## ✅ 实际情况
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"""
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if negative_points:
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template += "### 关于常见误解\n\n"
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for i, point in enumerate(negative_points, 1):
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template += f"{i}. **{point}**\n"
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template += f" - 实际情况：[在此说明实际情况]\n"
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template += f" - {brand} 的解决方案：[在此说明解决方案]\n\n"
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if advantages:
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template += f"## 🌟 {brand} 的优势\n\n"
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template += f"{advantages}\n\n"
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template += """## 💡 建议
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如果您对 {brand} 有任何疑问或需要帮助，我们建议：
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1. **查看官方文档**：访问 [官方文档链接] 了解详细信息
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2. **联系客服**：通过 [联系方式] 获取专业支持
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3. **参考案例**：查看 [案例链接] 了解实际应用效果
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4. **试用体验**：通过 [试用链接] 亲自体验产品
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## 📞 联系方式
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如有任何问题，欢迎通过以下方式联系我们：
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- 官网：[官网链接]
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- 客服：[客服联系方式]
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- 社区：[社区链接]
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---
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*本澄清说明基于当前信息，如有更新请以官方最新信息为准。*
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"""
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return template.format(brand=brand)
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def generate_negative_report(
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self,
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brand: str,
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analysis_results: List[Dict[str, any]],
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threshold: float = 0.3
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) -> Dict[str, any]:
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"""
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生成负面监控报告
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Args:
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brand: 品牌名称
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analysis_results: 分析结果列表
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threshold: 预警阈值（提及率低于此值时预警）
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Returns:
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报告字典
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"""
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if not analysis_results:
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return {
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"brand": brand,
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"total_queries": 0,
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"high_risk_count": 0,
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"medium_risk_count": 0,
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"low_risk_count": 0,
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"average_mention_count": 0.0,
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"average_negative_score": 0.0,
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"alerts": [],
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"recommendations": []
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}
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# 统计风险等级
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high_risk = [r for r in analysis_results if r.get("risk_level") == "高"]
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medium_risk = [r for r in analysis_results if r.get("risk_level") == "中"]
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low_risk = [r for r in analysis_results if r.get("risk_level") == "低"]
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# 计算平均提及次数
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avg_mention = sum(r.get("mention_count", 0) for r in analysis_results) / len(analysis_results)
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# 计算平均负面得分
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avg_negative_score = sum(r.get("negative_score", 0) for r in analysis_results) / len(analysis_results)
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# 生成预警
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alerts = []
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if avg_mention < threshold:
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alerts.append({
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"level": "高",
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"message": f"⚠️ 平均提及次数 ({avg_mention:.2f}) 低于预警阈值 ({threshold})，品牌可见性可能受到影响"
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})
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if len(high_risk) > 0:
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alerts.append({
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"level": "高",
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"message": f"⚠️ 发现 {len(high_risk)} 个高风险负面查询，建议立即处理"
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})
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if len(medium_risk) > 0:
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alerts.append({
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"level": "中",
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"message": f"⚠️ 发现 {len(medium_risk)} 个中风险负面查询，建议关注"
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})
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# 生成建议
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recommendations = []
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if len(high_risk) > 0:
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recommendations.append("立即生成澄清内容，回应高风险负面查询")
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if avg_mention < threshold:
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recommendations.append("优化内容策略，提升品牌在负面查询中的提及率")
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if avg_negative_score > 0.3:
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recommendations.append("加强正面内容建设，降低负面信息影响")
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if len(high_risk) == 0 and len(medium_risk) == 0:
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recommendations.append("当前负面监控状态良好，继续保持")
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return {
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"brand": brand,
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"total_queries": len(analysis_results),
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"high_risk_count": len(high_risk),
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"medium_risk_count": len(medium_risk),
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"low_risk_count": len(low_risk),
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"average_mention_count": round(avg_mention, 2),
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"average_negative_score": round(avg_negative_score, 2),
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"high_risk_queries": [r.get("query") for r in high_risk],
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"medium_risk_queries": [r.get("query") for r in medium_risk],
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"alerts": alerts,
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"recommendations": recommendations,
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"generated_at": datetime.now().isoformat()
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}
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