feat(pipeline): 按细类生成评价正/负向主题并写入报告8.3

开启 llm_comment_sentiment 时对每个矩阵细类单独构建情感载荷并调用模型；报告新增 8.3 小节，与探针及细类评论要点归纳并列。补充 jd_report 对 build_comment_sentiment_llm_payload 的导出、comment_sentiment_llm.json schema_version 2 与 groups 记录。前端表单说明与演示脚本注释同步更新。

Made-with: Cursor

backend/pipeline/competitor_report/jd_report.py

@@ -58,21 +58,15 @@ from pipeline.competitor_report.price_promo import (  # noqa: E402
     _markdown_price_promotion_section,
 )
 from pipeline.competitor_report.comment_sentiment import (  # noqa: E402
-    _comment_keyword_hits,
-    _iter_comment_text_units,
     _iter_comment_text_units_and_scores,
-    _merge_comment_previews,
     _parse_comment_score,
+    build_comment_sentiment_llm_payload,
 )
 from pipeline.competitor_report.llm_group_payloads import (  # noqa: E402
     build_comment_groups_llm_payload,
     build_matrix_groups_llm_payload,
     build_price_groups_llm_payload,
     build_promo_groups_llm_payload,
-    build_scenario_groups_llm_payload,
-    _comment_scenario_counts,
-    _group_keyword_hits,
-    _text_hits_scenario_triggers,
 )
 from pipeline.competitor_report.matrix_group import (  # noqa: E402
     _category_mix,
@@ -100,13 +94,10 @@ from pipeline.competitor_report.matrix_md import (  # noqa: E402
 )
 from pipeline.competitor_report.report_md_helpers import (  # noqa: E402
     _embed_chart,
-    _focus_scenario_combo_bar_filename,
     _lines_4_reading_brand,
     _lines_4_reading_shop,
     _matrix_prices_sales_chart_filename,
-    _mermaid_pie_focus_keywords,
     _scenario_group_asset_slug,
-    _scenario_summary_bullets,
     _strategy_hints,
 )
 from pipeline.competitor_report.run_context import (  # noqa: E402
@@ -117,7 +108,7 @@ from pipeline.competitor_report.run_context import (  # noqa: E402
 )
 
 # ---------------------------------------------------------------------------
-# 运行配置（按需改这里；与 pipeline.competitor_report.constants 中默认关注词等配合使用）
+# 运行配置（按需改这里；外部市场表等见 constants）
 # ---------------------------------------------------------------------------
 # KEYWORD：京东 PC 搜索词；全量抓取时必填。「仅已有目录」模式下可留空，改从 run_meta / 目录名推断。
 KEYWORD = "低GI"
@@ -148,7 +139,7 @@ def build_competitor_markdown(
     llm_strategy_opportunities_section_md: str | None = None,
     chapter8_text_mining_probe_section_md: str | None = None,
 ) -> str:
-    focus_words, scenario_groups, external_rows = resolve_report_tuning(report_config)
+    (external_rows,) = resolve_report_tuning(report_config)
     _ch8_probe_sec = (chapter8_text_mining_probe_section_md or "").strip()
     sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
     title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
@@ -210,23 +201,6 @@ def build_competitor_markdown(
     )
     promo_sig = _analyze_price_promotions(promo_rows)
 
-    hits = _comment_keyword_hits(comment_rows, focus_words)
-    if not hits:
-        blob = _merge_comment_previews(merged_rows)
-        for w in focus_words:
-            if len(w) < 2:
-                continue
-            n = blob.count(w)
-            if n:
-                hits[w] += n
-
-    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
-        comment_rows, merged_rows
-    )
-    scen_counts, scen_n_texts = _comment_scenario_counts(
-        comment_texts, scenario_groups
-    )
-
     feedback_groups = _consumer_feedback_by_matrix_group(
         merged_rows=merged_rows,
         comment_rows=comment_rows,
@@ -286,12 +260,11 @@ def build_competitor_markdown(
             "",
             "- **价格**：自页面「标价 / 券后价 / 详情价」等抽取的**展示价**，含促销与规格差异，**不等于**出厂价或成本。**第六章** 在具备可用的搜索列表导出时，优先以**列表全量**统计；否则使用**已深入 SKU** 的合并数据；**第六章第一节** 归纳标价与券后价差等**列表侧展示价差信号**（不对卖点/腰带字段做预设关键词扫描）。",
             "- **品牌/店铺集中度（第四章）**：有列表全量时按列表行计店铺与品牌占比；无列表导出时按深入 SKU 合并表估算。",
-            "- **评价主题词**：对评价正文做**预设词表子串计数**，非分词主题模型，适合扫方向，**需抽样人工验证**。",
+            "- **评价文本**：**不再**使用「预设关注词 / 预设场景词组」子串计数作为报告或简报主指标；**第八章第二节**以中文分词与开放词表统计（词频、关键词突出度、共现、主题探索等，可选词云及大模型解读）为主，结论须结合抽样原文理解。",
-            "- **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）**：**不再**使用星级子集内**预设口语短语**条形图、正负面扇形图及同口径摘要；**第八章第二节**为分词、词频、共现与主题等**文本补充分析**（可选词云及大模型解读），结论须结合抽样原文理解。"
+                "- **用户画像（第八章）**：**不再**使用星级子集内**预设口语短语**条形图、正负面扇形图及同口径摘要；**第八章第二节**为上述**文本补充分析**（探针）。"
                 if _ch8_probe_sec
-                else "- **用户画像（第八章）**：关注词与场景**仅按细类**以**同图左右并列**展示（左为关注词命中次数，右为场景占有效文本 **%**）；见**第八章第二节**。"
+                else "- **用户画像（第八章）**：若未启用探针，第二节仅说明方法并列出按细类评价条数；**请开启** `chapter8_text_mining_probe` 以生成开放词表统计与插图。"
             ),
             "- **细类划分（第五至第八章）**：**仅**依据合并表中的**商品详情页类目路径**；该信息缺失或无法读出细类名称的 SKU **不参与**竞品矩阵与按细类评价统计（相关评价条亦**不进入**按细类图表）。",
             "- **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。",
@@ -384,17 +357,15 @@ def build_competitor_markdown(
             exec_bullets.append(
                 f"列表侧约 **{100.0 * float(sh):.0f}%** 可对齐行呈现「券后/到手」**低于**「标价」，展示价差中位数约 **{float(med):.1f}%**（**第六章第一节** 活动与话术摘录）。"
             )
-    if multi_feedback_cat and (hits or scen_n_texts > 0):
+    if multi_feedback_cat and n_cmt > 0:
-        exec_bullets.append(
+        if _ch8_probe_sec:
-            "评价侧写（关注词、用途/场景）已按**第五章同一细类划分**分节，见**第八章第二节**（同图并列）。"
+            exec_bullets.append(
-        )
+                "评价侧写已按**第五章同一细类划分**呈现，见**第八章第二节**（文本挖掘探针）。"
-    elif hits:
+            )
-        top3 = "、".join(f"「{w}」({n})" for w, n in hits.most_common(3))
+        else:
-        exec_bullets.append(f"评价侧写（词频）：{top3}。")
+            exec_bullets.append(
-    if scen_n_texts > 0 and scen_counts and not multi_feedback_cat:
+                "评价已按**第五章同一细类**归组；**第八章第二节**定量主题分析依赖探针，可在报告调参中开启 `chapter8_text_mining_probe`。"
-        top_s = scen_counts.most_common(4)
+            )
-        frag = "；".join(f"{lbl} **{n}** 条" for lbl, n in top_s)
-        exec_bullets.append(f"用途/场景（评价自述，可多选）：{frag}（有效文本 **{scen_n_texts}** 条）。")
     if api_rc is not None:
         exec_bullets.append(
             f"PC 搜索返回的检索结果规模约 **{api_rc:,}**（站内匹配条数量级，见第三章第二节；**不是**零售额或动销统计）。"
@@ -750,9 +721,9 @@ def build_competitor_markdown(
         "- **细类划分**：与**第五章「竞品矩阵」**相同，**仅**依据合并表中的**商品详情页类目路径**解析为「饼干 / 西式糕点 / …」等（规则见第五章开头说明）。",
         "- **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；**在合并表中但该 SKU 缺少类目路径或读不出细类名称的，该评价不进入按细类统计**（与第五章**同一条排除规则**）。",
         (
-            "- **评论文本补充分析（第八章第二节）**：本任务已用中文分词与统计工具做了开放词表分析（词频、关键词突出度、词对共现、主题归纳等，可选词云），**不再**在报告中使用「星级子集内预设口语短语」条形图、正负面扇形图及同口径摘要；**不再**输出原「关注词次数 + 场景占比」左右并列条图。"
+            "- **评论文本补充分析（第八章第二节）**：本任务已用中文分词与统计工具做了开放词表分析（词频、关键词突出度、词对共现、主题归纳等，可选词云），**不再**在报告中使用「星级子集内预设口语短语」条形图、正负面扇形图及同口径摘要；**不再**使用「预设关注词 + 预设场景词组」子串计数与并列条图。"
             if _ch8_probe_sec
-            else "- **关注词与使用场景（第八章第二节）**：对组内评价正文做关注词子串计数（左栏条形图）；对每条有效文本独立扫描**本次任务生效的场景词组**（来自报告调参或系统默认），一条可属多场景，右栏为**占该细类有效文本比例 %**（多标签下可相加 **>** 100%）。二者在 **同一张图左右并列**，与第五章矩阵细类一一对应。"
+            else "- **评论文本补充分析（第八章第二节）**：本任务**未**嵌入探针正文；**不再**输出预设关注词/场景子串统计图。请在 `report_config` 中开启 `chapter8_text_mining_probe` 并重跑以生成开放词表统计与插图。"
         ),
         "",
     ]
@@ -769,15 +740,12 @@ def build_competitor_markdown(
             ]
         )
     else:
-        # 仅当未嵌入第八章第二节探针补充分析（_ch8_probe_sec 为空）时：原「关注词 + 场景」条图与逐细类段落
         lines.extend(
             [
-                "### 8.2 关注词与使用场景（按细类）",
+                "### 8.2 评论文本补充分析（未启用探针时的占位）",
                 "",
-                "每细类一张**左右并列图**（与报告附图文件夹中的 ``chart_focus_and_scenarios_bar__*.png`` 同源）："
+                "> **说明**：本版本**不再**生成「预设关注词 + 预设场景」子串统计与 ``chart_focus_and_scenarios_bar__*.png``。"
-                "**左**为配置关注词子串命中次数（同一评价可出现多次，为次数而非去重条数）；"
+                "开放词表、词频、共现与主题等请开启 ``chapter8_text_mining_probe`` 后重跑。",
-                "**右**为预设场景词组命中占该细类有效文本比例 %（一条可属多场景；多柱比例可相加 **>** 100%）。"
-                "统计均基于评价正文（或兜底预览）子串规则，**不等于**购买动机调研结论。",
                 "",
             ]
         )
@@ -785,73 +753,44 @@ def build_competitor_markdown(
             lines.append("*无评价数据可归组。*")
             lines.append("")
         else:
-            for gi, (gname, cr_g, texts_g) in enumerate(feedback_groups):
+            for _gi, (gname, cr_g, texts_g) in enumerate(feedback_groups):
                 n_flat = len(cr_g)
                 lines.append(f"#### {gname}")
                 lines.append("")
                 lines.append(
-                    f"- **本细类逐条评价**：{n_flat} 条；**用于统计的有效文本条数**：{len(texts_g)}。"
+                    f"- **本细类逐条评价**：{n_flat} 条；**有效文本单元**：{len(texts_g)}。"
                 )
                 lines.append("")
-                hits_g = _group_keyword_hits(cr_g, texts_g, focus_words=focus_words)
-                scen_g, scen_ng = _comment_scenario_counts(texts_g, scenario_groups)
-                has_focus = any(n > 0 for n in hits_g.values()) if hits_g else False
-                has_scen = scen_ng > 0 and any(n > 0 for n in scen_g.values())
-                if scen_ng <= 0:
-                    lines.append("*该细类下无可用评价正文。*")
-                    lines.append("")
-                    continue
-                if has_focus or has_scen:
-                    cap = (
-                        f"「{_md_cell(gname, 24)}」细类 · 关注词与使用场景（左：关注词命中次数；右：场景占有效文本 %；"
-                        f"有效文本 **{scen_ng}** 条）"
-                    )
-                    lines.extend(
-                        _embed_chart(
-                            run_dir,
-                            _focus_scenario_combo_bar_filename(gname, gi),
-                            cap,
-                        )
-                    )
-                else:
-                    lines.append("*该细类无关注词命中且未命中预设场景词组。*")
-                    lines.append("")
-                if has_scen:
-                    for para in _scenario_summary_bullets(scen_g, scen_ng):
-                        lines.append(para)
-                        lines.append("")
-                elif scen_ng > 0:
-                    lines.append("*未命中预设场景词组。*")
-                    lines.append("")
-
-        _llm_sg = (llm_scenario_groups_section_md or "").strip()
-        if _llm_sg:
-            lines.extend(
-                [
-                    "",
-                    "#### 使用场景要点归纳（大模型，与第八章第二节右栏图表互补）",
-                    "",
-                    "> **说明**：与第八章第二节**相同**的预设场景词组与子串命中规则；**各场景条数与占比以正文图右栏为准**。",
-                    "",
-                    _llm_sg,
-                    "",
-                ]
-            )
 
         _llm_cg = (llm_comment_groups_section_md or "").strip()
         if _llm_cg:
             lines.extend(
                 [
                     "",
-                    "#### 细类评价与关注词要点归纳（大模型，与第八章第二节左栏图表互补）",
+                    "#### 细类评论要点归纳（大模型，基于评价摘录）",
                     "",
-                    "> **说明**：归纳各细类反馈主题与配置关注词命中；**次数与第八章第二节图左栏以正文为准**。",
+                    "> **说明**：仅依据输入中的评价短摘录与文本单元归纳，**不**依赖预设关注词表或场景触发词统计。",
                     "",
                     _llm_cg,
                     "",
                 ]
             )
 
+    _llm_sent = (llm_sentiment_section_md or "").strip()
+    if _llm_sent:
+        lines.extend(
+            [
+                "",
+                "### 8.3 评价正/负向主题（按细类 · 大模型）",
+                "",
+                "> **说明**：与 **8.2 评论文本补充分析（探针）** 及 **「细类评论要点归纳（大模型）」** 并列；每段仅使用该细类下评价原文做语境归纳（如正向体验主题、负向评价主题归因）。"
+                "**不替代**探针的开放词表与专用口径，也不替代「细类评论要点归纳」的写法与载荷。",
+                "",
+                _llm_sent,
+                "",
+            ]
+        )
+
     lines.extend(["---", "", "## 九、策略与机会提示（假设清单，待验证）", ""])
     _llm_st = (llm_strategy_opportunities_section_md or "").strip()
     if _llm_st:
@@ -917,7 +856,7 @@ def build_competitor_brief(
     """
     与 ``build_competitor_markdown`` 共用**同一套统计规则**，输出可 JSON 序列化的结构化竞品摘要（**规则驱动**，无 LLM）。
     """
-    focus_words, scenario_groups, _ext = resolve_report_tuning(report_config)
+    (_ext,) = resolve_report_tuning(report_config)
     sku_header = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
     title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
     batch = _run_batch_label(run_dir)
@@ -975,22 +914,9 @@ def build_competitor_brief(
     )
     price_promotion_signals = _analyze_price_promotions(promo_rows_brief)
 
-    hits = _comment_keyword_hits(comment_rows, focus_words)
+    comment_texts, _comment_scores = _iter_comment_text_units_and_scores(
-    if not hits:
-        blob = _merge_comment_previews(merged_rows)
-        for w in focus_words:
-            if len(w) < 2:
-                continue
-            n = blob.count(w)
-            if n:
-                hits[w] += n
-
-    comment_texts, comment_scores = _iter_comment_text_units_and_scores(
         comment_rows, merged_rows
     )
-    scen_counts, scen_n_texts = _comment_scenario_counts(
-        comment_texts, scenario_groups
-    )
 
     (
         api_rc,
@@ -1005,10 +931,7 @@ def build_competitor_brief(
     hints = _strategy_hints(
         cr1=cr1_hints,
         pst=pst,
-        hits=hits,
         n_comments=n_cmt,
-        scen_counts=scen_counts,
-        scen_n_texts=scen_n_texts,
     )
 
     matrix_groups: list[dict[str, Any]] = []
@@ -1047,7 +970,6 @@ def build_competitor_brief(
         )
 
     feedback_by_group: list[dict[str, Any]] = []
-    usage_scenarios_by_matrix_group: list[dict[str, Any]] = []
     for gi, (gname, cr, tu) in enumerate(
         _consumer_feedback_by_matrix_group(
             merged_rows=merged_rows,
@@ -1055,8 +977,6 @@ def build_competitor_brief(
             sku_header=sku_header,
         )
     ):
-        gh = _group_keyword_hits(cr, tu, focus_words=focus_words)
-        scen_g, scen_n_g = _comment_scenario_counts(tu, scenario_groups)
         slug_fb = _scenario_group_asset_slug(gname, gi)
         feedback_by_group.append(
             {
@@ -1065,43 +985,8 @@ def build_competitor_brief(
                 "chart_slug": slug_fb,
                 "comment_rows": len(cr),
                 "effective_comment_text_units": len(tu),
-                "focus_keyword_hits": [
-                    {"word": w, "count": n} for w, n in gh.most_common(24)
-                ],
-                "scenarios_top": [
-                    {
-                        "scenario": s,
-                        "count": n,
-                        "share_of_text_units": (
-                            n / scen_n_g if scen_n_g else 0.0
-                        ),
-                    }
-                    for s, n in scen_g.most_common(6)
-                ]
-                if scen_n_g
-                else [],
             }
         )
-        if scen_n_g > 0 and scen_g:
-            usage_scenarios_by_matrix_group.append(
-                {
-                    "group": gname,
-                    "matrix_group_index": gi,
-                    "chart_slug": slug_fb,
-                    "effective_text_units": scen_n_g,
-                    "scenarios": [
-                        {
-                            "scenario": s,
-                            "count": int(n),
-                            "share_of_text_units": (
-                                float(n) / scen_n_g if scen_n_g else 0.0
-                            ),
-                        }
-                        for s, n in scen_g.most_common()
-                        if n > 0
-                    ],
-                }
-            )
 
     meta_slice: dict[str, Any] = {}
     if meta:
@@ -1188,26 +1073,15 @@ def build_competitor_brief(
         "price_stats_merged_sample": pst_merged,
         "price_stats_list_export": pst_list if list_export else {},
         "price_promotion_signals": price_promotion_signals,
-        "comment_focus_keywords": [
+        "comment_focus_keywords": [],
-            {"word": w, "count": n} for w, n in hits.most_common(24)
+        "usage_scenarios": [],
-        ],
+        "usage_scenarios_denominator": len(comment_texts),
-        "usage_scenarios": [
+        "usage_scenarios_by_matrix_group": [],
-            {
-                "scenario": lbl,
-                "count": n,
-                "share_of_text_units": (
-                    n / scen_n_texts if scen_n_texts else 0.0
-                ),
-            }
-            for lbl, n in scen_counts.most_common(16)
-        ],
-        "usage_scenarios_denominator": scen_n_texts,
-        "usage_scenarios_by_matrix_group": usage_scenarios_by_matrix_group,
         "strategy_hints": hints,
         "matrix_by_group": matrix_groups,
         "consumer_feedback_by_matrix_group": feedback_by_group,
         "notes": [
-            "与在线分析报告各章**计数规则**一致；关注词与场景以任务中的分析规则为准（子串命中统计，非深度主题模型）。",
+            "与在线分析报告各章**计数规则**一致；**不再**输出预设关注词/场景子串统计，评论侧主题以第八章文本挖掘探针（若启用）为准。",
             "价格来自页面展示字段抽取，含促销与规格差异；促销与标价对齐等为启发式摘录，仅供对照。",
             "「集中度」中：默认按**列表行**计数（同一 SKU 多页曝光会重复计）；`shops_from_list.unique_sku_basis` 为按**去重 SKU** 的对照口径。二者均**不是**销量、库存或全渠道市场份额。",
         ],

backend/pipeline/demos/run_report_llm_chapters_demo.py

@@ -3,9 +3,8 @@
 
 - §5 后：``generate_matrix_group_summaries_llm``
 - §6 后：``generate_price_group_summaries_llm``、``generate_promo_group_summaries_llm``
-- （可选、默认关闭）``generate_comment_sentiment_analysis_llm``：已不再嵌入报告正文
+- （可选、默认关闭）``generate_comment_sentiment_analysis_llm``：按细类多次调用，写入报告 **8.3**（与探针 / 评论要点归纳并列）
 - §8末细类评价：``generate_comment_group_summaries_llm``
-- §8.2 关注词/场景路径下右栏后使用场景：``generate_scenario_group_summaries_llm``
 - §9 策略与机会：``generate_strategy_opportunities_llm``（``build_competitor_brief`` + 可选 ``chapter_llm_narratives`` 与各章归纳对齐）
 - §8.5 类全文补充（独立长文）：``generate_competitor_report_markdown_llm``
 
@@ -132,7 +131,7 @@ def main() -> None:
         "--only",
         type=str,
         default="",
-        help="逗号分隔子集：sentiment,matrix,price,promo,strategy_opp,scenario_groups,comment_groups,report_supplement",
+        help="逗号分隔子集：sentiment,matrix,price,promo,strategy_opp,comment_groups,report_supplement",
     )
     parser.add_argument(
         "--preview-chars",
@@ -153,7 +152,6 @@ def main() -> None:
         "price",
         "promo",
         "strategy_opp",
-        "scenario_groups",
         "comment_groups",
         "report_supplement",
     }
@@ -182,8 +180,6 @@ def main() -> None:
         generate_promo_group_summaries_llm,
         generate_promo_group_summaries_llm_chunked,
         generate_strategy_opportunities_llm,
-        generate_scenario_group_summaries_llm,
-        generate_scenario_group_summaries_llm_chunked,
     )
 
     chunk_gr = use_chunked_group_summaries_llm(eff_rc)
@@ -313,41 +309,6 @@ def main() -> None:
                 flush=True,
             )
 
-    if "scenario_groups" in only:
-        _, scen_tuple, _ = jcr.resolve_report_tuning(eff_rc)
-        fb_s = jcr._consumer_feedback_by_matrix_group(
-            merged_rows=merged,
-            comment_rows=comment_rows,
-            sku_header=sku_h,
-        )
-        pl_sg = jcr.build_scenario_groups_llm_payload(
-            feedback_groups=fb_s,
-            scenario_groups=scen_tuple,
-            merged_rows=merged,
-            sku_header=sku_h,
-            title_h=title_h,
-        )
-
-        def _sg() -> str:
-            if chunk_gr:
-                return generate_scenario_group_summaries_llm_chunked(
-                    pl_sg, keyword=keyword
-                )
-            return generate_scenario_group_summaries_llm(pl_sg, keyword=keyword)
-
-        _run_one(
-            "§8.2 路径 · 使用场景归纳（scenario_groups）",
-            _sg,
-            live=args.live,
-            preview_chars=args.preview_chars,
-        )
-        if not args.live:
-            n = len((pl_sg or {}).get("groups") or [])
-            print(
-                f"  payload groups={n} chunked_by_matrix={chunk_gr}",
-                flush=True,
-            )
-
     if "comment_groups" in only:
         fb = jcr._consumer_feedback_by_matrix_group(
             merged_rows=merged,

backend/pipeline/jd/runner.py

@@ -170,7 +170,6 @@ def get_default_report_config() -> dict[str, Any]:
         "llm_comment_sentiment": False,
         "llm_matrix_group_summaries": True,
         "llm_comment_group_summaries": True,
-        "llm_scenario_group_summaries": True,
         "llm_price_group_summaries": True,
         "llm_promo_group_summaries": True,
         "llm_strategy_opportunities": True,
@@ -179,11 +178,6 @@ def get_default_report_config() -> dict[str, Any]:
         "chapter8_text_mining_probe_live_llm": True,
         "chapter8_text_mining_probe_llm_chunked": True,
         "chapter8_text_mining_probe_wordcloud": True,
-        "comment_focus_words": list(jcr.COMMENT_FOCUS_WORDS),
-        "comment_scenario_groups": [
-            {"label": lbl, "triggers": list(trs)}
-            for lbl, trs in jcr.COMMENT_SCENARIO_GROUPS
-        ],
         "external_market_table_rows": [
             {"indicator": a, "value_and_scope": b, "source": c, "year": d}
             for a, b, c, d in jcr.EXTERNAL_MARKET_TABLE_ROWS
@@ -263,10 +257,8 @@ def write_competitor_analysis_for_run_dir(
             )
             brief_slice = {
                 "keyword": brief_pre.get("keyword"),
-                "comment_focus_keywords": (
+                "comment_focus_keywords": [],
-                    brief_pre.get("comment_focus_keywords") or []
+                "usage_scenarios": [],
-                )[:20],
-                "usage_scenarios": (brief_pre.get("usage_scenarios") or [])[:8],
                 "category_mix_top": (brief_pre.get("category_mix_top") or [])[:6],
                 "scope": brief_pre.get("scope"),
             }
@@ -276,13 +268,6 @@ def write_competitor_analysis_for_run_dir(
                 all_comment_texts=all_tx,
             )
             suggest_record.update(sug)
-            base_words = list(eff_rc.get("comment_focus_words") or [])
-            for w in sug.get("suggested_focus_keywords") or []:
-                if isinstance(w, str):
-                    t = w.strip()
-                    if t and t not in base_words:
-                        base_words.append(t)
-            eff_rc["comment_focus_words"] = base_words[:80]
         except Exception as e:
             suggest_record["error"] = str(e)
             suggest_record["suggested_focus_keywords"] = []
@@ -290,60 +275,8 @@ def write_competitor_analysis_for_run_dir(
         suggest_record["skipped"] = True
         suggest_record["suggested_focus_keywords"] = []
 
-    skip_scen = os.environ.get("MA_SKIP_LLM_SCENARIO_SUGGEST", "").strip().lower() in (
+    suggest_record["suggested_scenario_groups"] = []
-        "1",
+    suggest_record["scenario_note"] = "预设场景词组已废弃，不再写入 report_config。"
-        "true",
-        "yes",
-    )
-    if not skip_scen:
-        try:
-            from ..llm.keyword_suggest import suggest_scenario_groups_llm
-
-            raw_sg = eff_rc.get("comment_scenario_groups")
-            if isinstance(raw_sg, list) and raw_sg:
-                scen_base = [x for x in raw_sg if isinstance(x, dict)]
-            else:
-                scen_base = [
-                    {"label": lbl, "triggers": list(trs)}
-                    for lbl, trs in jcr.COMMENT_SCENARIO_GROUPS
-                ]
-            scen_out = suggest_scenario_groups_llm(
-                keyword=kw,
-                existing_groups=scen_base,
-                all_comment_texts=all_tx,
-            )
-            suggest_record["suggested_scenario_groups"] = scen_out.get(
-                "suggested_scenario_groups"
-            ) or []
-            suggest_record["scenario_rationale"] = scen_out.get("scenario_rationale") or ""
-            exist_labels = {
-                str(x.get("label") or "").strip().lower()
-                for x in scen_base
-                if str(x.get("label") or "").strip()
-            }
-            merged_scen = list(scen_base)
-            for g in suggest_record["suggested_scenario_groups"]:
-                if not isinstance(g, dict):
-                    continue
-                lab = str(g.get("label") or "").strip()
-                tr_in = g.get("triggers")
-                triggers: list[str] = []
-                if isinstance(tr_in, list):
-                    for t in tr_in[:48]:
-                        s = str(t).strip()
-                        if 2 <= len(s) <= 48:
-                            triggers.append(s)
-                if not lab or lab.lower() in exist_labels or len(triggers) < 2:
-                    continue
-                merged_scen.append({"label": lab[:80], "triggers": triggers[:48]})
-                exist_labels.add(lab.lower())
-            eff_rc["comment_scenario_groups"] = merged_scen[:40]
-        except Exception as e:
-            suggest_record["scenario_error"] = str(e)
-            suggest_record["suggested_scenario_groups"] = []
-    else:
-        suggest_record["scenario_skipped"] = True
-        suggest_record["suggested_scenario_groups"] = []
 
     suggest_path.write_text(
         json.dumps(suggest_record, ensure_ascii=False, indent=2),
@@ -369,8 +302,9 @@ def write_competitor_analysis_for_run_dir(
 
     llm_sentiment_md = ""
     sentiment_llm_record: dict[str, Any] = {
-        "schema_version": 1,
+        "schema_version": 2,
         "attempted": False,
+        "groups": [],
     }
     skip_sent = os.environ.get(
         "MA_SKIP_LLM_COMMENT_SENTIMENT", ""
@@ -382,42 +316,81 @@ def write_competitor_analysis_for_run_dir(
     )
     want_sent = bool(eff_rc.get("llm_comment_sentiment")) or env_on
     if want_sent and not skip_sent:
-        comment_units, comment_scores = jcr._iter_comment_text_units_and_scores(
+        feedback_groups_sg = jcr._consumer_feedback_by_matrix_group(
-            comment_rows, merged_rows
+            merged_rows=merged_rows,
+            comment_rows=comment_rows,
+            sku_header=MERGED_FIELD_TO_CSV_HEADER["sku_id"],
         )
-        if len(comment_units) >= 2:
+        if not feedback_groups_sg:
-            sentiment_llm_record["attempted"] = True
+            sentiment_llm_record["skipped"] = "no_feedback_groups"
-            try:
-                from ..llm.generate import generate_comment_sentiment_analysis_llm
-
-                attr_units = jcr._comment_lines_with_product_context(
-                    comment_rows,
-                    merged_rows,
-                    sku_header=MERGED_FIELD_TO_CSV_HEADER["sku_id"],
-                    title_h=MERGED_FIELD_TO_CSV_HEADER["title"],
-                )
-                if len(attr_units) != len(comment_units):
-                    attr_units = list(comment_units)
-                pl = jcr.build_comment_sentiment_llm_payload(
-                    comment_units,
-                    scores=comment_scores,
-                    attributed_texts=attr_units,
-                    max_samples_positive=16,
-                    max_samples_negative=30,
-                    max_samples_mixed=10,
-                    max_chars_per_review=360,
-                    semantic_pool_max=40,
-                    shuffle_seed=kw,
-                )
-                pl["keyword"] = kw
-                llm_sentiment_md = generate_comment_sentiment_analysis_llm(pl)
-                sentiment_llm_record["ok"] = True
-                sentiment_llm_record["chars"] = len(llm_sentiment_md)
-            except Exception as e:
-                sentiment_llm_record["ok"] = False
-                sentiment_llm_record["error"] = str(e)
         else:
-            sentiment_llm_record["skipped"] = "insufficient_comment_texts"
+            sentiment_llm_record["attempted"] = True
+            from ..llm.generate import generate_comment_sentiment_analysis_llm
+
+            parts_sent: list[str] = []
+            grp_logs: list[dict[str, Any]] = []
+            sku_h = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
+            title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
+            for gname, cr_g, _tu in feedback_groups_sg:
+                sub_merged = [
+                    r
+                    for r in merged_rows
+                    if jcr._competitor_matrix_group_key(r) == gname
+                ]
+                units, scores = jcr._iter_comment_text_units_and_scores(
+                    cr_g, sub_merged
+                )
+                if len(units) < 2:
+                    grp_logs.append(
+                        {
+                            "group": gname,
+                            "skipped": "insufficient_comment_texts",
+                            "n_texts": len(units),
+                        }
+                    )
+                    continue
+                try:
+                    attr_units = jcr._comment_lines_with_product_context(
+                        cr_g,
+                        merged_rows,
+                        sku_header=sku_h,
+                        title_h=title_h,
+                    )
+                    if len(attr_units) != len(units):
+                        attr_units = list(units)
+                    pl = jcr.build_comment_sentiment_llm_payload(
+                        units,
+                        scores=scores,
+                        attributed_texts=attr_units,
+                        max_samples_positive=16,
+                        max_samples_negative=30,
+                        max_samples_mixed=10,
+                        max_chars_per_review=360,
+                        semantic_pool_max=40,
+                        shuffle_seed=f"{kw}|{gname}",
+                    )
+                    pl["keyword"] = kw
+                    pl["matrix_group_focus"] = gname
+                    md_one = generate_comment_sentiment_analysis_llm(pl)
+                    parts_sent.append(f"#### {gname}\n\n{md_one.strip()}\n")
+                    grp_logs.append(
+                        {"group": gname, "ok": True, "chars": len(md_one)}
+                    )
+                except Exception as e:
+                    grp_logs.append({"group": gname, "ok": False, "error": str(e)})
+            sentiment_llm_record["groups"] = grp_logs
+            llm_sentiment_md = "\n".join(parts_sent).strip()
+            sentiment_llm_record["chars"] = len(llm_sentiment_md)
+            if llm_sentiment_md:
+                sentiment_llm_record["ok"] = True
+            else:
+                sentiment_llm_record["ok"] = False
+                if grp_logs and all("skipped" in x for x in grp_logs):
+                    sentiment_llm_record["skipped"] = "insufficient_comment_texts"
+                elif grp_logs and any("error" in x for x in grp_logs):
+                    sentiment_llm_record["error"] = "all_groups_failed_or_skipped"
+                else:
+                    sentiment_llm_record["error"] = "no_output"
     elif skip_sent:
         sentiment_llm_record["skipped"] = "MA_SKIP_LLM_COMMENT_SENTIMENT"
     elif not want_sent:
@@ -431,7 +404,6 @@ def write_competitor_analysis_for_run_dir(
     llm_matrix_md = ""
     llm_price_md = ""
     llm_promo_md = ""
-    llm_scenario_gr_md = ""
     llm_comment_gr_md = ""
     llm_strategy_opp_md = ""
     matrix_llm_rec: dict[str, Any] = {"schema_version": 1, "attempted": False}
@@ -449,7 +421,6 @@ def write_competitor_analysis_for_run_dir(
     skip_mx = _env_on("MA_SKIP_LLM_MATRIX_GROUP_SUMMARIES")
     skip_pr = _env_on("MA_SKIP_LLM_PRICE_GROUP_SUMMARIES")
     skip_po = _env_on("MA_SKIP_LLM_PROMO_GROUP_SUMMARIES")
-    skip_sg = _env_on("MA_SKIP_LLM_SCENARIO_GROUP_SUMMARIES")
     skip_cg = _env_on("MA_SKIP_LLM_COMMENT_GROUP_SUMMARIES")
     want_mx = bool(eff_rc.get("llm_matrix_group_summaries")) or _env_on(
         "MA_ENABLE_LLM_MATRIX_GROUP_SUMMARIES"
@@ -460,9 +431,6 @@ def write_competitor_analysis_for_run_dir(
     want_po = bool(eff_rc.get("llm_promo_group_summaries")) or _env_on(
         "MA_ENABLE_LLM_PROMO_GROUP_SUMMARIES"
     )
-    want_sg = bool(eff_rc.get("llm_scenario_group_summaries")) or _env_on(
-        "MA_ENABLE_LLM_SCENARIO_GROUP_SUMMARIES"
-    )
     want_cg = bool(eff_rc.get("llm_comment_group_summaries")) or _env_on(
         "MA_ENABLE_LLM_COMMENT_GROUP_SUMMARIES"
     )
@@ -512,7 +480,6 @@ def write_competitor_analysis_for_run_dir(
             ch8_probe_rec["error"] = str(e)
 
     if use_ch8_probe and chapter8_probe_embed_md:
-        want_sg = False
         want_cg = False
 
     chunk_gr = use_chunked_group_summaries_llm(eff_rc)
@@ -625,53 +592,7 @@ def write_competitor_analysis_for_run_dir(
     elif not want_po:
         promo_llm_rec["skipped"] = "not_enabled"
 
-    if want_sg and not skip_sg and merged_rows:
+    scenario_gr_llm_rec["skipped"] = "preset_scenario_summaries_removed"
-        _, scenario_tuple, _ = jcr.resolve_report_tuning(eff_rc)
-        fb_sg = jcr._consumer_feedback_by_matrix_group(
-            merged_rows=merged_rows,
-            comment_rows=comment_rows,
-            sku_header=sku_h,
-        )
-        pl_sg = jcr.build_scenario_groups_llm_payload(
-            feedback_groups=fb_sg,
-            scenario_groups=scenario_tuple,
-            merged_rows=merged_rows,
-            sku_header=sku_h,
-            title_h=title_h,
-        )
-        if pl_sg:
-            scenario_gr_llm_rec["attempted"] = True
-            try:
-                if chunk_gr:
-                    from ..llm.generate import (
-                        generate_scenario_group_summaries_llm_chunked,
-                    )
-
-                    llm_scenario_gr_md = generate_scenario_group_summaries_llm_chunked(
-                        pl_sg, keyword=kw
-                    )
-                else:
-                    from ..llm.generate import generate_scenario_group_summaries_llm
-
-                    llm_scenario_gr_md = generate_scenario_group_summaries_llm(
-                        pl_sg, keyword=kw
-                    )
-                scenario_gr_llm_rec["ok"] = True
-                scenario_gr_llm_rec["chars"] = len(llm_scenario_gr_md)
-                scenario_gr_llm_rec["chunked_by_matrix"] = chunk_gr
-                if chunk_gr:
-                    scenario_gr_llm_rec["chunk_count"] = len(
-                        (pl_sg.get("groups") or [])
-                    )
-            except Exception as e:
-                scenario_gr_llm_rec["ok"] = False
-                scenario_gr_llm_rec["error"] = str(e)
-        else:
-            scenario_gr_llm_rec["skipped"] = "empty_scenario_groups_payload"
-    elif skip_sg:
-        scenario_gr_llm_rec["skipped"] = "MA_SKIP_LLM_SCENARIO_GROUP_SUMMARIES"
-    elif not want_sg:
-        scenario_gr_llm_rec["skipped"] = "not_enabled"
 
     if want_cg and not skip_cg and merged_rows:
         fb_cg = jcr._consumer_feedback_by_matrix_group(
@@ -729,8 +650,6 @@ def write_competitor_analysis_for_run_dir(
                 _strategy_narratives["sec6_price_group_summaries"] = llm_price_md
             if (llm_promo_md or "").strip():
                 _strategy_narratives["sec6_promo_group_summaries"] = llm_promo_md
-            if (llm_scenario_gr_md or "").strip():
-                _strategy_narratives["sec8_3_scenario_summaries"] = llm_scenario_gr_md
             if use_ch8_probe and (chapter8_probe_embed_md or "").strip():
                 _strategy_narratives["sec8_3_text_mining_probe"] = (
                     chapter8_probe_embed_md
@@ -802,7 +721,7 @@ def write_competitor_analysis_for_run_dir(
         llm_matrix_section_md=llm_matrix_md or None,
         llm_price_groups_section_md=llm_price_md or None,
         llm_promo_groups_section_md=llm_promo_md or None,
-        llm_scenario_groups_section_md=llm_scenario_gr_md or None,
+        llm_scenario_groups_section_md=None,
         llm_comment_groups_section_md=llm_comment_gr_md or None,
         llm_strategy_opportunities_section_md=llm_strategy_opp_md or None,
         chapter8_text_mining_probe_section_md=chapter8_probe_embed_md or None,

backend/pipeline/llm/generate_sections.py

@@ -32,12 +32,19 @@ SENTIMENT_LLM_SYSTEM = """你是电商/食品类用户研究助手。输入 JSON
 3. ``#### 混合评价中的典型张力``（可选）：同一评价里褒贬并存时，说明在争什么；若无则略写。
 4. ``#### 使用注意``：关键词子串统计的局限、``sample_reviews_semantic_pool`` 与词表归类的差异、抽样截断、非医学结论。
 
-总字数约 **700～1600 字**，简体中文，语气客观。"""
+**篇幅**：若 JSON 含 ``matrix_group_focus``（单细类范围），本节总字数约 **500～1200 字**，勿再按全关键词池写「全行业泛化」；若**不含**该字段（全量池），总字数约 **700～1600 字**。简体中文，语气客观。"""
 
 
 def generate_comment_sentiment_analysis_llm(payload: dict[str, Any]) -> str:
     """基于 lexicon 统计 + 语义池与按词表归类的抽样，生成评价情感归纳段落（Markdown）；**默认不**嵌入竞品报告正文。"""
     p = dict(payload)
+    scope_note = ""
+    mg = p.get("matrix_group_focus")
+    if isinstance(mg, str) and mg.strip():
+        scope_note = (
+            f"\n\n【范围】以下评价与统计**仅**来自细类「{mg.strip()}」；"
+            "正向/负向主题须贴合**该细类**语境，勿笼统写成「全关键词下用户普遍…」。\n"
+        )
     raw = json.dumps(p, ensure_ascii=False)
     if len(raw) > 88_000:
         for k, cap, maxlen in (
@@ -52,7 +59,7 @@ def generate_comment_sentiment_analysis_llm(payload: dict[str, Any]) -> str:
         raw = json.dumps(p, ensure_ascii=False)
     if len(raw) > 88_000:
         raw = raw[:82_000] + "\n\n…（输入过长已截断，请勿编造截断外内容）\n"
-    user = "请根据以下 JSON 按系统说明输出 Markdown：\n\n" + raw
+    user = "请根据以下 JSON 按系统说明输出 Markdown：" + scope_note + "\n\n" + raw
     return call_llm(SENTIMENT_LLM_SYSTEM, user)
 
 

backend/pipeline/tests/test_jd_report_sentiment_section.py

@@ -0,0 +1,28 @@
+"""竞品报告 Markdown：第八章情感小节嵌入（烟测）。"""
+from __future__ import annotations
+
+import tempfile
+from pathlib import Path
+
+from django.test import SimpleTestCase
+
+from pipeline.competitor_report import jd_report as jcr
+
+
+class JdReportSentimentSectionTests(SimpleTestCase):
+    def test_chapter_83_embeds_when_sentiment_md_provided(self) -> None:
+        with tempfile.TemporaryDirectory() as td:
+            run_dir = Path(td)
+            (run_dir / "report_assets").mkdir(parents=True)
+            md = jcr.build_competitor_markdown(
+                run_dir=run_dir,
+                keyword="测试词",
+                merged_rows=[],
+                search_export_rows=[],
+                comment_rows=[],
+                meta=None,
+                llm_sentiment_section_md="#### 饼干\n\n- 正向\n",
+            )
+        self.assertIn("### 8.3 评价正/负向主题（按细类 · 大模型）", md)
+        self.assertIn("不替代**探针的开放词表", md)
+        self.assertIn("#### 饼干", md)

frontend/src/components/ReportConfigFormFields.vue

@@ -1,55 +1,24 @@
 <script setup>
 defineProps({
-  focusWordRows: { type: Array, required: true },
-  scenarioGroups: { type: Array, required: true },
   marketRows: { type: Array, required: true },
 })
-defineEmits([
+defineEmits(['add-market', 'remove-market'])
-  'add-focus',
-  'remove-focus',
-  'add-scenario',
-  'remove-scenario',
-  'add-market',
-  'remove-market',
-])
 </script>
 
 <template>
   <div>
     <div class="rc-section">
-      <h4 class="rc-subtitle">1. 评价里要统计的「关注词」</h4>
+      <h4 class="rc-subtitle">1. 第八章评论分析</h4>
-      <p class="rc-help">报告会数这些词在评价里出现了多少次（适合看大家常提什么，例如口感、控糖、价格等）。</p>
-      <div class="rc-rows">
-        <div v-for="(row, i) in focusWordRows" :key="'f' + i" class="rc-inline">
-          <input v-model="row.text" type="text" class="rc-input" placeholder="输入一个词，如：控糖" />
-          <button type="button" class="ma-btn ma-btn-secondary rc-mini" @click="$emit('remove-focus', i)">删除</button>
-        </div>
-      </div>
-      <button type="button" class="ma-btn ma-btn-secondary rc-add" @click="$emit('add-focus')">添加词</button>
-    </div>
-
-    <div class="rc-section">
-      <h4 class="rc-subtitle">2. 用途 / 场景分组</h4>
       <p class="rc-help">
-        每一组有一个<strong>场景名称</strong>，和若干<strong>触发词</strong>。若一条评价里出现了其中任意一个词，这条评价就会算进该场景（一条评价可以同时属于多个场景）。触发词请用顿号、逗号或换行分开。
+        报告<strong>不再</strong>使用「预设关注词 / 预设场景词组」子串统计。请在报告配置（高级 JSON 或接口）中维护
+        <code>chapter8_text_mining_probe</code>
+        等开关，以生成开放词表、词频与共现等文本挖掘内容。可选
+        <code>llm_comment_sentiment</code>：按矩阵细类分别调用模型，在报告<strong>8.3</strong>生成「正/负向主题」归纳，与探针及「细类评论要点归纳」并列、互不替代。
       </p>
-      <div v-for="(g, i) in scenarioGroups" :key="'s' + i" class="rc-scenario-card">
-        <label class="rc-label">场景名称</label>
-        <input v-model="g.label" type="text" class="rc-input" placeholder="如：早餐 / 代餐" />
-        <label class="rc-label">触发词</label>
-        <textarea
-          v-model="g.triggersText"
-          class="rc-textarea"
-          rows="2"
-          placeholder="如：早餐、代餐、早饭（可用顿号或换行分隔）"
-        />
-        <button type="button" class="ma-btn ma-btn-secondary rc-mini" @click="$emit('remove-scenario', i)">删除本组</button>
-      </div>
-      <button type="button" class="ma-btn ma-btn-secondary rc-add" @click="$emit('add-scenario')">添加场景组</button>
     </div>
 
     <div class="rc-section">
-      <h4 class="rc-subtitle">3. 外部市场信息（可选）</h4>
+      <h4 class="rc-subtitle">2. 外部市场信息（可选）</h4>
       <p class="rc-help">若手边有第三方市场规模、增速等摘录，可填在表里，报告会多一节说明；不需要可整表留空。</p>
       <div class="rc-market-wrap">
         <table class="rc-market">
@@ -107,17 +76,6 @@ defineEmits([
   color: #6b7280;
   line-height: 1.5;
 }
-.rc-rows {
-  display: flex;
-  flex-direction: column;
-  gap: 0.45rem;
-}
-.rc-inline {
-  display: flex;
-  flex-wrap: wrap;
-  align-items: center;
-  gap: 0.5rem;
-}
 .rc-input {
   flex: 1;
   min-width: 140px;
@@ -128,7 +86,6 @@ defineEmits([
   font-size: 0.88rem;
   box-sizing: border-box;
 }
-/* 表内输入：不占 flex，宽度受列约束，避免撑进邻列 */
 .rc-input.rc-td {
   flex: none;
   display: block;
@@ -137,28 +94,6 @@ defineEmits([
   max-width: 100%;
   font-size: 0.8rem;
 }
-.rc-textarea {
-  width: 100%;
-  max-width: 100%;
-  box-sizing: border-box;
-  padding: 0.45rem 0.55rem;
-  border: 1px solid #d1d5db;
-  border-radius: 6px;
-  font: inherit;
-  font-size: 0.88rem;
-  resize: vertical;
-  margin: 0.35rem 0 0.5rem;
-}
-.rc-label {
-  display: block;
-  font-size: 0.8rem;
-  font-weight: 500;
-  color: #4b5563;
-  margin-top: 0.35rem;
-}
-.rc-label:first-of-type {
-  margin-top: 0;
-}
 .rc-mini {
   font-size: 0.8rem;
   padding: 0.3rem 0.55rem;
@@ -168,13 +103,6 @@ defineEmits([
   margin-top: 0.5rem;
   font-size: 0.85rem;
 }
-.rc-scenario-card {
-  background: #fff;
-  border: 1px solid #e5e7eb;
-  border-radius: 8px;
-  padding: 0.75rem 0.85rem;
-  margin-bottom: 0.65rem;
-}
 .rc-market-wrap {
   overflow-x: auto;
   margin-bottom: 0.35rem;