Reddit Community Signal Mining — Reddit 社区信号挖掘与品牌口碑监测

"""
Reddit Community Signal Mining — 母婴社区品牌信号挖掘
基于 arXiv: 2505.20185 (2025) + arXiv: 2508.05107 (CIKM 2025)

依赖: re, statistics, dataclasses (标准库)
生产环境: 替换 MockRedditData 为 Reddit API (PRAW)
"""

from dataclasses import dataclass, field
from statistics import mean
import re


@dataclass
class RedditPost:
    """Reddit 帖子数据结构"""
    post_id: str
    subreddit: str
    title: str
    body: str
    score: int              # Upvote 数
    num_comments: int
    created_utc: float
    author_flair: str = ""  # 用户标签（IBCLC/Pediatrician 等）


@dataclass
class BrandSignal:
    """品牌在 Reddit 的信号摘要"""
    brand: str
    total_mentions: int
    avg_sentiment: float
    high_score_mentions: int    # score > 100 的帖子数
    expert_mentions: int        # 专家账号提及数
    top_issues: list            # 最高频的负面问题
    top_praises: list           # 最高频的正面评价
    ai_citation_risk: float     # AI 引用风险分（高分 = 更可能被 AI 引用）


class SentimentAnalyzer:
    """简单的情感分析器（生产环境替换为 ABSA 模型）"""

    POS_WORDS = {"love", "great", "amazing", "quiet", "perfect", "recommend",
                 "excellent", "easy", "comfortable", "worth", "best"}
    NEG_WORDS = {"hate", "terrible", "loud", "leak", "broken", "expensive",
                 "useless", "difficult", "poor", "waste", "regret", "return"}
    NEG_PREFIX = {"not", "no", "never", "don't", "doesn't", "isn't", "wasn't"}

    def score(self, text: str) -> float:
        words = re.findall(r'\b\w+\b', text.lower())
        pos, neg = 0, 0
        for i, w in enumerate(words):
            prefix = words[i-1] if i > 0 else ""
            if w in self.POS_WORDS:
                pos += 1 if prefix not in self.NEG_PREFIX else -1
            elif w in self.NEG_WORDS:
                neg += 1
        total = pos + neg
        if total == 0: return 0.0
        return (pos - neg) / total