xbuilderlab--cheat-on-content
wehub-resource-sync
114 行
4.7 KiB
Python
114 行
4.7 KiB
Python
"""从渲染后的 DOM 文本里抽取 LinkedIn 单帖分析指标。
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LinkedIn 把单帖分析（/analytics/post-summary/）SSR/inline 进页面——不是可拦的
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voyager XHR，所以读 `inner_text` 文本、按已知标签锚点解析。纯函数，可独立测试
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（见 test_extract.py）。
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LinkedIn 会在**日文 / 英文之间随机切换**界面语言（同一 session 内都可能换），
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所以每个指标都存多语言别名，两套都试。
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"""
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from __future__ import annotations
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import re
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def _to_int(s: str) -> int | None:
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"""'34,057' → 34057；'1.2K' → 1200；'3M' → 3000000；抽不出返回 None。"""
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s = s.strip().replace(",", "")
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m = re.fullmatch(r"([\d.]+)\s*([KMB]?)", s)
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if not m:
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return None
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mult = {"": 1, "K": 1_000, "M": 1_000_000, "B": 1_000_000_000}[m.group(2)]
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try:
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return int(float(m.group(1)) * mult)
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except ValueError:
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return None
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# 单帖分析页（/analytics/post-summary/）。LinkedIn 在 日/英 间**随机切换**语言，
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# 所以每个指标存多语言别名。版式两段：
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# 顶部指标——值在标签上一行（"before"）
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# 互动明细——值在标签下一行（"after"）
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# (labels, key, value_position)
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POST_METRICS = [
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(("インプレッション数", "Impressions"), "impressions", "before"),
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(("リーチしたメンバー", "Members reached"), "reach", "before"),
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(("この投稿からのプロフィール閲覧ユーザー", "Profile viewers from this post"), "profile_views_from_post", "before"),
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(("この投稿で獲得したフォロワー", "Followers gained from this post"), "followers_from_post", "before"),
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(("ソーシャルエンゲージメント", "Social engagements"), "social_engagement", "before"),
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(("リアクション", "Reactions"), "reactions", "after"),
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(("コメント", "Comments"), "comments", "after"),
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(("再投稿", "Reposts"), "reposts", "after"),
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(("保存数", "Saves"), "saves", "after"),
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(("LinkedInでの送信数", "Sends on LinkedIn"), "sends", "after"),
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]
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_IMPRESSION_LABELS = ("インプレッション数", "Impressions")
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# 作者署名行的语言标记（"…さんが投稿しました • 4日" / "… posted this • 6d"）。
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_BYLINE_MARKERS = ("さんが投稿しました", "posted this")
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# 顶部指标段之前会出现的小标题（"Discovery" / "調査" 等）——正文与指标的分界。
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_BODY_END_MARKERS = ("調査", "Discovery", "ディスカバリー")
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def parse_post_summary(text: str) -> dict:
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"""单帖分析 DOM 文本 → {'metrics': {...}}。支持 日/英（LinkedIn 随机切换）。
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锚在第一个指标（Impressions）后，避开正文里的数字；每个指标按 value_position
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取前一行 / 后一行。抽不到的指标为 None（接口/版式变更时不致整体崩）。
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"""
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lines = [l.strip() for l in text.splitlines() if l.strip()]
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start = 0
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for i, l in enumerate(lines):
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if l in _IMPRESSION_LABELS:
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start = max(0, i - 1)
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break
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scan = lines[start:]
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out = {key: None for _, key, _ in POST_METRICS}
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for i, line in enumerate(scan):
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for labels, key, pos in POST_METRICS:
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if line not in labels or out[key] is not None:
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continue
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j = i - 1 if pos == "before" else i + 1
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if 0 <= j < len(scan):
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out[key] = _to_int(scan[j])
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return {"metrics": out}
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def parse_post_meta(text: str) -> dict:
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"""从单帖分析 DOM 抽作者署名 + 相对发布时间 + 正文。
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版式（reading order）：署名行（"…さんが投稿しました • 4日" / "… posted this • 6d"）
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→ 正文若干行 → 顶部指标小标题（"Discovery"/"調査"）→ 指标段。
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取署名行与小标题之间的行为正文；抽不到时各字段为空，不报错。
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返回 {'author': str, 'age': str, 'text': str}。
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"""
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lines = [l.strip() for l in text.splitlines() if l.strip()]
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byline_idx = None
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for i, l in enumerate(lines):
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if any(m in l for m in _BYLINE_MARKERS):
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byline_idx = i
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break
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if byline_idx is None:
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return {"author": "", "age": "", "text": ""}
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byline = lines[byline_idx]
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author, age = "", ""
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if "•" in byline:
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head, _, tail = byline.partition("•")
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age = tail.strip()
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author = head.strip()
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else:
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author = byline.strip()
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for marker in _BYLINE_MARKERS:
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if marker in author:
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author = author.split(marker, 1)[0].strip()
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break
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body: list[str] = []
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for l in lines[byline_idx + 1:]:
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if l in _IMPRESSION_LABELS or l in _BODY_END_MARKERS:
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break
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body.append(l)
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return {"author": author, "age": age, "text": "\n".join(body).strip()}
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