cedfd066c4
新增垃圾信息细分类标签，在朴素贝叶斯二分类基础上对spam进行细分： - 新增 spam_categorizer.py 分类模块（诈骗/骚扰/广告） - SpamPredictionLog 和 ContentPost 模型添加 category 字段 - content_routes 和 spam_routes 接口返回分类标签 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
"""垃圾信息分类标签模块
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在朴素贝叶斯二分类（spam/ham）基础上，对判定为 spam 的文本进行细分类标签。
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分类优先级：诈骗 > 骚扰 > 广告（按危害程度排序）
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"""
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CATEGORY_KEYWORDS = {
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"fraud": [
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"中奖", "幸运粉丝", "幸运用户", "银行卡异常", "社保异常", "账号冻结",
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"解封", "立即验证", "验证码", "欠费停机", "退款待确认", "违章信息",
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"紧急通知", "账户异常", "风险", "核验", "被冻结", "将被冻结",
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],
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"harassment": [
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"兼职", "日结", "高薪", "刷单", "赚钱", "外快", "宝妈", "学生都能做",
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"添加微信", "扫码进群", "进群立刻", "想赚", "零花钱", "在家办公",
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"无需面试", "火热招募", "秒赚", "招募",
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],
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"advertisement": [
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"领取", "优惠", "红包", "优惠券", "秒杀", "返现", "补贴", "会员",
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"特价", "低价", "点击链接", "扫码", "免费领取", "无门槛", "现金券",
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"盲盒", "百分百中奖", "隐藏优惠券", "内部价", "货到付款", "限时",
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"最后", "名额", "先到先得",
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],
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}
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CATEGORY_LABELS = {
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"fraud": "疑似诈骗",
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"harassment": "疑似骚扰",
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"advertisement": "疑似广告",
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"spam": "疑似垃圾",
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"ham": "",
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}
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CATEGORY_PRIORITY = ["fraud", "harassment", "advertisement"]
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def categorize_spam(text: str) -> tuple[str, str]:
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"""根据关键词匹配判定垃圾信息的具体分类标签
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Args:
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text: 待分类的文本内容
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Returns:
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tuple[str, str]: (category_code, category_label)
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- category_code: fraud | harassment | advertisement | spam
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- category_label: 疑似诈骗 | 疑似骚扰 | 疑似广告 | 疑似垃圾
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"""
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text_lower = text.lower()
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for category in CATEGORY_PRIORITY:
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keywords = CATEGORY_KEYWORDS.get(category, [])
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for kw in keywords:
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if kw.lower() in text_lower:
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return category, CATEGORY_LABELS[category]
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return "spam", CATEGORY_LABELS["spam"]
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def get_category_label(category: str) -> str:
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"""获取分类标签的中文显示文本
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Args:
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category: 分类代码
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Returns:
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str: 中文标签文本
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"""
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return CATEGORY_LABELS.get(category, "") |