mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-22 08:01:34 +08:00
Revert "docs(report): note LLM keyword/scenario extension in competitor brief"
This reverts commit 70b0b247f4d8ffd66120237ee6154f993fb2d733.
This commit is contained in:
hub-gif
parent
30a985f749
commit
4cae885200
@ -3,7 +3,7 @@
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关键词 → 调用 ``jd_keyword_pipeline`` 全链路采集 → 生成 **标准化竞品分析报告**（Markdown）。
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报告结构对齐常见竞品分析框架：研究范围与方法、执行摘要、**整体市场观察（列表可见度 proxy）**、
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市场与竞争结构、**按细分类目分组的竞品结构（统计图 + 合并表外置）**、价格分析、产品与宣称、**按细分类目的消费者反馈与用户画像**、策略提示与附录；并明确数据边界。
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市场与竞争结构、**按细分类目分组的竞品对比矩阵**、价格分析、产品与宣称、**按细分类目的消费者反馈与用户画像**、策略提示与附录；并明确数据边界。
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若运行配置中提供了外部市场规模摘录（``EXTERNAL_MARKET_TABLE_ROWS``），则追加对应表格小节；否则不输出占位行。
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依赖：全量抓取时与 ``jd_keyword_pipeline.py`` 相同（Node、h5st、Playwright、``common/jd_cookie.txt``）。
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@ -281,27 +281,6 @@ def _collect_prices(rows: list[dict[str, str]]) -> list[float]:
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return out
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def _label_count_dicts_top_n_plus_other(
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labels: list[str], *, top_n: int, other_label: str
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) -> list[dict[str, Any]]:
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"""
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Top-N 标签计数 + 一条「其他」汇总剩余行数，使 ``count`` 之和等于非空标签行总数
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（与 §4 集中度表、扇形图分母一致；否则仅 ``most_common(N)`` 会丢掉长尾店铺/品牌行）。
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"""
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cleaned = [(x or "").strip() for x in labels if (x or "").strip()]
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if not cleaned:
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return []
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cnt = Counter(cleaned)
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total_rows = len(cleaned)
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mc = cnt.most_common(top_n)
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out: list[dict[str, Any]] = [{"label": k, "count": int(v)} for k, v in mc]
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covered = sum(v for _, v in mc)
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rest = total_rows - covered
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if rest > 0:
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out.append({"label": other_label, "count": int(rest)})
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return out
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_JD_LIST_PRICE_KEY = "标价(jdPrice,jdPriceText,realPrice)"
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_COUPON_SHOW_PRICE_KEY = (
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"券后到手价(couponPrice,subsidyPrice,finalPrice.estimatedPrice,priceShow)"
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@ -542,84 +521,6 @@ def _iter_comment_text_units(
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return out
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def _context_tag_escape(s: str) -> str:
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"""避免破坏 ``【细类…】`` 定界符。"""
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return (s or "").replace("】", "]").replace("｜", "|").replace("\n", " ").strip()
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def _comment_context_prefix(
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*,
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matrix_group: str,
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sku: str,
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title: str,
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shop: str,
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) -> str:
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g = _context_tag_escape(matrix_group)[:40]
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sk = _context_tag_escape(sku)[:22]
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tit = _context_tag_escape(title)[:56]
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sh = _context_tag_escape(shop)[:36]
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return f"【细类：{g}｜SKU：{sk}｜品名：{tit}｜店铺：{sh}】"
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def _merged_by_sku(
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merged_rows: list[dict[str, str]], sku_header: str
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) -> dict[str, dict[str, str]]:
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out: dict[str, dict[str, str]] = {}
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for row in merged_rows:
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sku = _cell(row, sku_header).strip()
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if sku and sku not in out:
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out[sku] = row
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return out
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def _comment_lines_with_product_context(
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comment_rows: list[dict[str, str]],
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merged_rows: list[dict[str, str]],
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*,
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sku_header: str,
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title_h: str,
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) -> list[str]:
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"""
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与 :func:`_iter_comment_text_units` **同序、同条数**（逐条评价），仅在正文前加归属头，
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供 §8.2 等大模型抽样；**情感词表统计**仍须用无头正文 :func:`_iter_comment_text_units`。
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"""
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if not merged_rows:
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return list(_iter_comment_text_units(comment_rows, []))
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sku_map = _sku_to_matrix_group_map(merged_rows, sku_header)
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by_sku = _merged_by_sku(merged_rows, sku_header)
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out: list[str] = []
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for row in comment_rows:
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t = _cell(row, "tagCommentContent")
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if not t:
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continue
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sku = _cell(row, "sku").strip()
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g = sku_map.get(sku, "未归类（评价 SKU 无对应深入样本）")
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m = by_sku.get(sku) or {}
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prefix = _comment_context_prefix(
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matrix_group=g,
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sku=sku,
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title=_cell(m, title_h),
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shop=_cell(m, "店铺名(shopName)", "detail_shop_name"),
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)
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out.append(prefix + t)
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if out:
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return out
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for row in merged_rows:
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p = _cell(row, "comment_preview")
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if not p:
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continue
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sku = _cell(row, sku_header).strip()
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g = _competitor_matrix_group_key(row)
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prefix = _comment_context_prefix(
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matrix_group=g,
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sku=sku,
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title=_cell(row, title_h),
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shop=_cell(row, "店铺名(shopName)", "detail_shop_name"),
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)
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out.append(prefix + p)
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return out
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_POS_LEX = (
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"好",
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"赞",
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@ -799,57 +700,39 @@ def _comment_sentiment_lexicon(texts: list[str]) -> dict[str, Any]:
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def build_comment_sentiment_llm_payload(
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texts: list[str],
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*,
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attributed_texts: list[str] | None = None,
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max_samples_positive: int = 16,
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max_samples_negative: int = 30,
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max_samples_mixed: int = 10,
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max_chars_per_review: int = 360,
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max_chars_per_review: int = 300,
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) -> dict[str, Any]:
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"""
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供大模型做正/负向语义归纳：附规则统计与**去重后的评价原文抽样**（与 §8.2 词表分桶一致）。
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负向样本默认多于正向，便于大模型做「具体问题是什么」的主题归因，而非只复述词频。
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``attributed_texts`` 与 ``texts`` 须一一对齐；前者为带 ``【细类｜SKU｜品名｜店铺】`` 头的展示串，
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分桶与 ``comment_sentiment_lexicon`` 仍只基于 ``texts``（无头正文），避免品名营销词干扰粗判。
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"""
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attrs: list[str]
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if attributed_texts is not None and len(attributed_texts) == len(texts):
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attrs = list(attributed_texts)
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else:
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attrs = list(texts)
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pos_only_texts: list[str] = []
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neg_only_texts: list[str] = []
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mixed_texts: list[str] = []
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pos_attr_pairs: list[tuple[str, str]] = []
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neg_attr_pairs: list[tuple[str, str]] = []
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mixed_attr_pairs: list[tuple[str, str]] = []
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for plain, attr in zip(texts, attrs):
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s = (plain or "").strip()
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for t in texts:
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s = (t or "").strip()
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if not s:
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continue
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hp = any(k in s for k in _POS_CLASS)
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hn = any(k in s for k in _NEG_CLASS)
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a = (attr or plain).strip()
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if hp and hn:
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mixed_texts.append(s)
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mixed_attr_pairs.append((s, a))
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elif hp:
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pos_only_texts.append(s)
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pos_attr_pairs.append((s, a))
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elif hn:
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neg_only_texts.append(s)
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neg_attr_pairs.append((s, a))
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def _sample_pairs(pairs: list[tuple[str, str]], cap: int) -> list[str]:
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def _sample(seq: list[str], cap: int) -> list[str]:
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out: list[str] = []
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seen_plain: set[str] = set()
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for plain, attr in pairs:
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if plain in seen_plain:
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seen: set[str] = set()
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for raw in seq:
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if raw in seen:
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continue
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seen_plain.add(plain)
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raw = (attr or plain).strip()
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seen.add(raw)
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if len(raw) > max_chars_per_review:
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out.append(raw[:max_chars_per_review] + "…")
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else:
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@ -867,147 +750,12 @@ def build_comment_sentiment_llm_payload(
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"comment_sentiment_lexicon": lex,
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"positive_lexeme_hits_top": pos_h_top,
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"negative_lexeme_hits_top": neg_h_top,
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"sample_reviews_positive_biased": _sample_pairs(
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pos_attr_pairs, max_samples_positive
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),
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"sample_reviews_negative_biased": _sample_pairs(
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neg_attr_pairs, max_samples_negative
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),
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"sample_reviews_mixed_tone": _sample_pairs(
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mixed_attr_pairs, max_samples_mixed
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),
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"sample_attribution_note": (
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"每条样本行前【细类｜SKU｜品名｜店铺】来自合并表与矩阵分组；"
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"写负向体验时须让读者能对应到「哪家店、哪条 SKU/哪款品名」，勿脱离前缀另编店铺或品名。"
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),
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"sample_reviews_positive_biased": _sample(pos_only_texts, max_samples_positive),
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"sample_reviews_negative_biased": _sample(neg_only_texts, max_samples_negative),
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"sample_reviews_mixed_tone": _sample(mixed_texts, max_samples_mixed),
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}
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def build_comment_groups_llm_payload(
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*,
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feedback_groups: list[tuple[str, list[dict[str, str]], list[str]]],
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focus_words: tuple[str, ...],
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merged_rows: list[dict[str, str]],
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sku_header: str,
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title_h: str,
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) -> list[dict[str, Any]]:
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"""
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按矩阵细类组装「关注词/评价摘录」类 LLM 输入（细类块列表）。
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``sample_text_snippets`` 与 §8.2 抽样一致，带 ``【细类｜SKU｜品名｜店铺】`` 前缀。
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"""
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sku_map = _sku_to_matrix_group_map(merged_rows, sku_header) if merged_rows else {}
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by_sku = _merged_by_sku(merged_rows, sku_header) if merged_rows else {}
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out: list[dict[str, Any]] = []
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for gname, cr, tu in feedback_groups:
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snippets: list[str] = []
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for row in cr:
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t = _cell(row, "tagCommentContent")
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if not t:
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continue
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sku = _cell(row, "sku").strip()
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g = sku_map.get(sku, gname)
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m = by_sku.get(sku) or {}
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prefix = _comment_context_prefix(
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matrix_group=g,
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sku=sku,
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title=_cell(m, title_h),
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shop=_cell(m, "店铺名(shopName)", "detail_shop_name"),
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)
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snippets.append(prefix + t)
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if len(snippets) >= 8:
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break
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fh: list[str] = []
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blob = "\n".join(tu[:400])
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for w in focus_words:
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if len(w) < 2:
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continue
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n = blob.count(w)
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if n:
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fh.append(f"「{w}」×{n}")
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out.append(
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{
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"group": gname,
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"comment_flat_rows": len(cr),
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"effective_text_lines": len(tu),
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"focus_hit_lines": fh[:14],
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"sample_text_snippets": snippets,
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}
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)
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return out
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def _price_listing_snippet_for_llm(row: dict[str, str], *, title_h: str) -> str:
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"""供价盘大模型：标题与标价/券后/详情价摘录（与 §6 表格同源字段）。"""
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title = _cell(row, title_h).strip()
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jd = _cell(row, _JD_LIST_PRICE_KEY).strip()
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cp = _cell(row, _COUPON_SHOW_PRICE_KEY).strip()
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df = _cell(row, "detail_price_final").strip()
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return (
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f"{title[:80]}｜标价:{jd[:20]}｜券后:{cp[:20]}｜详情价:{df[:20]}"
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).strip("｜")
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def build_price_groups_llm_payload(
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merged_rows: list[dict[str, str]],
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*,
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sku_header: str,
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title_h: str,
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) -> list[dict[str, Any]]:
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"""按 §5 矩阵细类拆分价盘摘录，供 ``generate_price_group_summaries_llm``。"""
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out: list[dict[str, Any]] = []
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for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
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if not grows:
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continue
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prices = _collect_prices(grows)
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pst = _price_stats_extended(prices) if prices else {"n": 0}
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snippets: list[str] = []
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for r in grows[:14]:
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s = _price_listing_snippet_for_llm(r, title_h=title_h)
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if s.replace("｜", "").replace(":", "").strip():
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snippets.append(s)
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out.append(
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{
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"group": gname,
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"sku_count": len(grows),
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"price_stats": pst,
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"listing_snippets": snippets,
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}
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)
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return out
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def build_matrix_groups_llm_payload(
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merged_rows: list[dict[str, str]],
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*,
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sku_header: str,
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title_h: str,
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) -> list[dict[str, Any]]:
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"""按 §5 矩阵细类拆分卖点/配料摘录与价统计，供 ``generate_matrix_group_summaries_llm``。"""
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out: list[dict[str, Any]] = []
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for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
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if not grows:
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continue
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prices = _collect_prices(grows)
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pst = _price_stats_extended(prices) if prices else {"n": 0}
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lines: list[str] = []
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for r in grows[:18]:
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t = _md_cell(_cell(r, title_h), 56)
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sp = _md_cell(_cell(r, _SELLING_POINT_KEY), 80)
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ing = _matrix_ingredients_cell(r, max_len=100)
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part = f"{t}｜卖点:{sp}｜配料:{ing}"
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if part.replace("｜", "").strip():
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lines.append(part)
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out.append(
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{
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"group": gname,
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"sku_count": len(grows),
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"price_stats": pst,
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"lines": lines,
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}
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)
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return out
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def _mermaid_pie_focus_keywords(hits: Counter[str], *, top_k: int = 8) -> str:
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"""关注词全局 Top 的 Mermaid pie（便于渲染或导出工具识别）。"""
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top = hits.most_common(top_k)
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@ -1655,7 +1403,7 @@ def _lines_4_reading_category(
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"",
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f"- 列表侧可读类目/简称共 **{len(cm_structure)}** 种取值，合计 **{total}** 行；"
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f"其中「{_md_cell(top_lbl, 40)}」行数最多，约占 **{100 * share:.1f}%**。",
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"- 若头部类目占比极高，说明当前关键词下货架被少数品类定义；跨品类机会需结合 §5 细类结构与合并表再核对。",
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"- 若头部类目占比极高，说明当前关键词下货架被少数品类定义；跨品类机会需结合商详矩阵（§5）再核对。",
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"",
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"| 类目/简称（Top 5） | 列表行数 | 占本章结构样本 |",
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"| --- | ---: | ---: |",
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@ -1678,9 +1426,6 @@ def build_competitor_markdown(
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meta: dict[str, Any] | None,
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report_config: dict[str, Any] | None = None,
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llm_sentiment_section_md: str | None = None,
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llm_matrix_section_md: str | None = None,
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llm_price_groups_section_md: str | None = None,
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llm_comment_groups_section_md: str | None = None,
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) -> str:
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focus_words, scenario_groups, external_rows = resolve_report_tuning(report_config)
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sku_header = "SKU(skuId)"
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@ -1800,7 +1545,6 @@ def build_competitor_markdown(
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"- **用途/场景**：对每条评价独立判断是否命中预设场景词；一条可计入多个场景，统计的是「提及该场景的评价条数」而非用户数。",
|
||||
"- **用户画像（第八章）**：正负面粗判含**口语短语**级摘录；关注词与场景**仅按细类**以条形图展示（场景图为**占该细类有效文本比例 %**）；见 §8.3～8.4。",
|
||||
"- **各章衔接（可选）**：若任务配置 ``llm_section_bridges``（或部署侧环境变量启用），则在「## 一」至「## 九」各章二级标题后插入大模型撰写的**衔接分析**段落，便于阅读过渡；**定量结论仍以正文表格与摘要 JSON 为准**。",
|
||||
"- **细类大模型归纳（可选）**：若任务配置 ``llm_price_group_summaries`` / ``llm_comment_group_summaries``（或对应 ``MA_ENABLE_LLM_*`` 环境变量），则在 **§6.2、§8.6** 插入价盘/评论关注词的细类归纳；**§5 不再输出矩阵表**，卖点/配料等仍以 ``keyword_pipeline_merged.csv`` 与 JSON 为准。",
|
||||
"- **检索结果规模**：来自京东 PC 搜索返回的「结果条数」类指标，表示平台侧申报的匹配数量级，**不等于**动销、库存或独立 SKU 数。",
|
||||
"",
|
||||
"### 1.4 主要局限",
|
||||
@ -1863,7 +1607,7 @@ def build_competitor_markdown(
|
||||
)
|
||||
elif list_export and cr1_deep is not None and top_brand_deep and not brands_s:
|
||||
exec_bullets.append(
|
||||
f"列表导出缺少品牌标题字段，**深入 {n_sku} SKU** 商详品牌第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），供与 §5 细类结构对照。"
|
||||
f"列表导出缺少品牌标题字段，**深入 {n_sku} SKU** 商详品牌第一大品牌份额 ≈ **{100 * cr1_deep:.1f}%**（「{top_brand_deep}」），供与 §5 矩阵对照。"
|
||||
)
|
||||
if pst:
|
||||
price_src_short = (
|
||||
@ -2034,7 +1778,7 @@ def build_competitor_markdown(
|
||||
_embed_chart(
|
||||
run_dir,
|
||||
"chart_brand_rows_pie.png",
|
||||
"品牌列表曝光占比（扇形图；与上表「含品牌字段行数」同源，长尾已并入「其他」尾桶）",
|
||||
"品牌列表曝光占比（扇形图，Top 段合并为「其他」；与上表集中度同源）",
|
||||
)
|
||||
)
|
||||
lines.extend(
|
||||
@ -2053,7 +1797,7 @@ def build_competitor_markdown(
|
||||
lines.append(
|
||||
f"*列表导出中店铺/品牌标题有效 **{brand_rows_n}** 条，"
|
||||
f"低于建议阈值（≥{min_brand_rows}），品牌集中度未展开。**店铺结构见 §4.2**；"
|
||||
f"商详品牌在 **§5 细类结构**。*"
|
||||
f"商详品牌在 **§5**。*"
|
||||
)
|
||||
else:
|
||||
lines.append("*深入子样本无可用品牌字段。*")
|
||||
@ -2076,7 +1820,7 @@ def build_competitor_markdown(
|
||||
_embed_chart(
|
||||
run_dir,
|
||||
"chart_shop_rows_pie.png",
|
||||
"店铺列表曝光占比（扇形图；与上表「含店铺名的行数」同源，长尾已并入「其他」尾桶）",
|
||||
"店铺列表曝光占比（扇形图；与上表同源）",
|
||||
)
|
||||
)
|
||||
lines.extend(
|
||||
@ -2116,37 +1860,40 @@ def build_competitor_markdown(
|
||||
[
|
||||
"---",
|
||||
"",
|
||||
"## 五、竞品结构（按细分类目分组）",
|
||||
"## 五、竞品对比矩阵（按细分类目分组）",
|
||||
"",
|
||||
"SKU 依商详**类目路径**聚合为细类：**三级路径**取中间一段（如 … > **饼干** > 粗粮饼干），"
|
||||
"**四级及以上**取倒数第二段（如 … > **面条** > 挂面）；类目列为空时退化为列表类目或规格，仍无则「未归类」。",
|
||||
"优先按商详**类目路径**列分组：**三级路径**取中间一段（如 … > **饼干** > 粗粮饼干），"
|
||||
"**四级及以上**取倒数第二段（如 … > **面条** > 挂面）。若该列为空，退化为搜索列表中的类目或规格属性；仍无则「未归类」。全量合并模式下另有更多商详字段可供核对。",
|
||||
"",
|
||||
"**本版不再在正文打印细类矩阵表**（宽表影响阅读）；各 SKU 的标题、品牌、店铺、标价/详情价、卖点、配料、评价量与摘要等，"
|
||||
"请直接查看本批次目录下 ``keyword_pipeline_merged.csv``，或调用 API 结构化字段 ``matrix_by_group``。",
|
||||
"下列按细类给出 **SKU 款数** 及 **展示价 / 评价量** 统计图（与合并表、``matrix_by_group`` 同源）。",
|
||||
"维度说明：**产品**（标题/规格）、**价格**（列表展示）、**渠道**（京东店铺）、**推广**（卖点/榜单文案）、"
|
||||
"**类目**、**配料表**（见下）、**声量**（评价量与摘要）。",
|
||||
"",
|
||||
"**配料表**：优先使用配料正文列（开启配料视觉解析时为识别出的文字）；"
|
||||
"仅有详情长图链接时列内会提示；若商详参数含「配料/配料表：」则摘录该段。"
|
||||
"均为页面信息摘录，**以包装实物与法规标签为准**。",
|
||||
"",
|
||||
]
|
||||
)
|
||||
matrix_header = [
|
||||
"| SKU | 产品（标题） | 品牌 | 标价 | 详情价 | 渠道（店铺） | 推广（卖点） | 榜单/标签 | 类目 | 配料表 | 评价量(搜索) | 消费者反馈摘要 |",
|
||||
"| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
|
||||
]
|
||||
grouped_matrix = _merged_rows_grouped_for_matrix(merged_rows)
|
||||
if not grouped_matrix:
|
||||
lines.append("*无合并表 SKU。*")
|
||||
lines.append("")
|
||||
for gi, (gname, grows) in enumerate(grouped_matrix):
|
||||
for gname, grows in grouped_matrix:
|
||||
lines.append(f"### {gname}（**{len(grows)}** 款）")
|
||||
lines.append("")
|
||||
lines.append(
|
||||
"*SKU 级字段明细见 ``keyword_pipeline_merged.csv``（本细类 SKU 与上节分组规则一致）。*"
|
||||
)
|
||||
lines.append("")
|
||||
slug_mx = _scenario_group_asset_slug(gname, gi)
|
||||
lines.extend(
|
||||
_embed_chart(
|
||||
run_dir,
|
||||
f"chart_matrix_prices_reviews__{slug_mx}.png",
|
||||
f"「{_md_cell(gname, 24)}」细类 · **展示价与评价量**（条形图；与合并表及 ``matrix_by_group`` 同源："
|
||||
f"价取 detail_price_final→标价→券后 优先可解析数值；评价量为搜索侧「评价量」字段摘录）",
|
||||
lines.extend(matrix_header)
|
||||
grows_sorted = sorted(grows, key=lambda r: _cell(r, sku_header) or "")
|
||||
for row in grows_sorted:
|
||||
lines.append(
|
||||
_competitor_matrix_md_line(
|
||||
row, sku_header=sku_header, title_h=title_h
|
||||
)
|
||||
)
|
||||
)
|
||||
lines.append("")
|
||||
|
||||
ch6_price_title = (
|
||||
"## 六、价格分析（PC 搜索列表全量）"
|
||||
@ -2203,21 +1950,6 @@ def build_competitor_markdown(
|
||||
lines.extend(_markdown_price_promotion_section(promo_sig))
|
||||
lines.append("")
|
||||
|
||||
_pr_llm = (llm_price_groups_section_md or "").strip()
|
||||
if _pr_llm:
|
||||
lines.extend(
|
||||
[
|
||||
"",
|
||||
"### 6.2 细类价盘要点归纳（大模型）",
|
||||
"",
|
||||
"> **说明**：以下为模型按 §5 同细类拆分、仅基于上文本节价统计与价字段摘录的归纳（价带与价差）；"
|
||||
"不含配料/宣称/场景关键词分析。具体数值仍以 **§6** 表格及 ``keyword_pipeline_merged.csv`` 为准。",
|
||||
"",
|
||||
_pr_llm,
|
||||
"",
|
||||
]
|
||||
)
|
||||
|
||||
attrs: list[str] = []
|
||||
for row in merged_rows:
|
||||
a = _cell(row, "detail_product_attributes")
|
||||
@ -2239,7 +1971,7 @@ def build_competitor_markdown(
|
||||
"",
|
||||
"### 8.1 方法",
|
||||
"",
|
||||
"- **细类划分**：与 **§5** 相同的商详类目路径规则（见 §5 章首）。",
|
||||
"- **细类划分**：与 **§5 竞品矩阵** 相同，依据商详类目路径解析为「饼干 / 西式糕点 / …」等（规则见 §5 章首说明）。",
|
||||
"- **归因**：每条评价按其 SKU 对应到深入样本，再映射到该 SKU 所属细类；SKU 不在合并表中的评价单独归入说明性分组。",
|
||||
"- **正负面粗判（§8.2）**：先以关键词规则与图表做粗分；若任务开启 **llm_comment_sentiment**，可附**大模型对抽样原文的主题归因**（尤其负向「用户在抱怨什么」），与词频条形图互补。",
|
||||
"- **关注词按细类（§8.3）**：对组内评价正文做子串计数并出条形图；若无逐条正文则用该细类下评价摘要列拼接兜底；与配置关注词及联想扩展同源。",
|
||||
@ -2379,20 +2111,6 @@ def build_competitor_markdown(
|
||||
lines.append("*未命中预设场景词组。*")
|
||||
lines.append("")
|
||||
|
||||
_cg_llm = (llm_comment_groups_section_md or "").strip()
|
||||
if _cg_llm:
|
||||
lines.extend(
|
||||
[
|
||||
"",
|
||||
"### 8.6 细类评论与关注词要点归纳（大模型）",
|
||||
"",
|
||||
"> **说明**：按 §5 细类与 §8.3～8.4 同源关注词/场景统计；抽样行含店铺与 SKU 前缀。具体仍以条形图与 CSV 为准。",
|
||||
"",
|
||||
_cg_llm,
|
||||
"",
|
||||
]
|
||||
)
|
||||
|
||||
lines.extend(
|
||||
[
|
||||
"---",
|
||||
@ -2688,16 +2406,18 @@ def build_competitor_brief(
|
||||
"category_mix_top": [
|
||||
{"label": lbl, "count": cnt} for lbl, cnt in cm_structure
|
||||
],
|
||||
"list_brand_mix_top": _label_count_dicts_top_n_plus_other(
|
||||
brands_s,
|
||||
top_n=24,
|
||||
other_label="其他（Top24 以外品牌行数合计）",
|
||||
),
|
||||
"list_shop_mix_top": _label_count_dicts_top_n_plus_other(
|
||||
shops_s,
|
||||
top_n=24,
|
||||
other_label="其他（Top24 以外店铺行数合计）",
|
||||
),
|
||||
"list_brand_mix_top": [
|
||||
{"label": k, "count": v}
|
||||
for k, v in Counter(
|
||||
b for b in brands_s if (b or "").strip()
|
||||
).most_common(24)
|
||||
],
|
||||
"list_shop_mix_top": [
|
||||
{"label": k, "count": v}
|
||||
for k, v in Counter(
|
||||
s for s in shops_s if (s or "").strip()
|
||||
).most_common(24)
|
||||
],
|
||||
"price_stats": pst,
|
||||
"price_stats_source": price_stats_source,
|
||||
"price_stats_merged_sample": pst_merged,
|
||||
@ -2723,7 +2443,7 @@ def build_competitor_brief(
|
||||
"consumer_feedback_by_matrix_group": feedback_by_group,
|
||||
"comment_sentiment_lexicon": comment_sentiment_lexicon,
|
||||
"notes": [
|
||||
"与在线分析报告各章统计口径一致；关注词与场景以任务 report_config 为底，报告生成时还可经 keyword_suggest_llm 合并延伸（仍为非深度学习的子串命中统计）。",
|
||||
"与在线分析报告各章统计口径一致；主题词与场景为预设词表，非 NLP 主题模型。",
|
||||
"价格来自页面展示字段抽取，含促销与规格差异；price_promotion_signals 为标价/券后对齐与卖点话术的启发式摘录。",
|
||||
"comment_sentiment_lexicon 为关键词粗判，非深度学习情感模型。",
|
||||
],
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user