diff --git a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py index b74634f..96301c4 100644 --- a/backend/crawler_copy/jd_pc_search/jd_competitor_report.py +++ b/backend/crawler_copy/jd_pc_search/jd_competitor_report.py @@ -103,6 +103,11 @@ _COMMENT_FUZZ_KEYS: tuple[str, ...] = ( _COMMENT_CSV_SKU = COMMENT_CSV_COLUMNS[0] _COMMENT_CSV_BODY = COMMENT_CSV_COLUMNS[3] +_COMMENT_CSV_SCORE = COMMENT_CSV_COLUMNS[7] # 「评分」→ commentScore + +# 评价星级与 §8.2 分桶：先按评分筛正负，再在对应子集内统计口语短语（无评分时回退关键词） +_COMMENT_SCORE_NEG_MAX = 2 # 1～2 星 → 偏负向 +_COMMENT_SCORE_POS_MIN = 4 # 4～5 星 → 偏正向（3 星为中评，归入中性） # --------------------------------------------------------------------------- # 运行配置（按需改这里） @@ -561,18 +566,38 @@ def _merge_comment_previews(merged_rows: list[dict[str, str]]) -> str: return "\n".join(parts) -def _iter_comment_text_units( +def _parse_comment_score(val: Any) -> int | None: + """解析 ``commentScore`` /「评分」列；期望京东 1～5 星，非法或空返回 None。""" + s = str(val or "").strip() + if not s: + return None + m = re.match(r"^\s*(\d+(?:\.\d+)?)", s) + if not m: + return None + try: + x = float(m.group(1)) + except ValueError: + return None + if x < 1 or x > 5: + return None + return int(round(x)) + + +def _iter_comment_text_units_and_scores( comment_rows: list[dict[str, str]], merged_rows: list[dict[str, str]], -) -> list[str]: - """逐条评价正文；无 flat 评论时用合并表 comment_preview 按行兜底。""" - out: list[str] = [] +) -> tuple[list[str], list[int | None]]: + """逐条评价正文与同序评分（无则为 None）；无 flat 评论时用合并表 comment_preview 按行兜底（评分均为 None）。""" + texts: list[str] = [] + scores: list[int | None] = [] for row in comment_rows: t = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent") - if t: - out.append(t) - if out: - return out + if not t: + continue + texts.append(t) + scores.append(_parse_comment_score(_cell(row, _COMMENT_CSV_SCORE, "commentScore"))) + if texts: + return texts, scores for row in merged_rows: p = _cell( row, @@ -580,8 +605,18 @@ def _iter_comment_text_units( "comment_preview", ) if p: - out.append(p) - return out + texts.append(p) + scores.append(None) + return texts, scores + + +def _iter_comment_text_units( + comment_rows: list[dict[str, str]], + merged_rows: list[dict[str, str]], +) -> list[str]: + """逐条评价正文；无 flat 评论时用合并表 comment_preview 按行兜底。""" + texts, _ = _iter_comment_text_units_and_scores(comment_rows, merged_rows) + return texts _POS_LEX = ( @@ -732,38 +767,133 @@ def _lexeme_hits_in_texts( return [{"word": w, "texts_matched": n} for w, n in c.most_common(18)] -def _comment_sentiment_lexicon(texts: list[str]) -> dict[str, Any]: +def _keyword_sentiment_quadrant(stripped: str) -> str: + """``pos_only`` | ``neg_only`` | ``mixed`` | ``neutral``（空文本为 neutral）。""" + if not stripped: + return "neutral" + hp = any(k in stripped for k in _POS_CLASS) + hn = any(k in stripped for k in _NEG_CLASS) + if hp and hn: + return "mixed" + if hp: + return "pos_only" + if hn: + return "neg_only" + return "neutral" + + +def _sentiment_quadrant_for_row( + stripped: str, + score: int | None, + *, + use_score_column: bool, +) -> str: + if not stripped: + return "neutral" + if use_score_column and score is not None: + if score <= _COMMENT_SCORE_NEG_MAX: + return "neg_only" + if score >= _COMMENT_SCORE_POS_MIN: + return "pos_only" + return "neutral" + return _keyword_sentiment_quadrant(stripped) + + +def _include_in_positive_lexeme_corpus( + stripped: str, + score: int | None, + *, + use_score_column: bool, +) -> bool: + """口语短语正向统计语境：评分模式下为 4～5 星；否则为命中正向词（含原「混合」条）。""" + if not stripped: + return False + if use_score_column and score is not None: + return score >= _COMMENT_SCORE_POS_MIN + hp = any(k in stripped for k in _POS_CLASS) + return hp + + +def _include_in_negative_lexeme_corpus( + stripped: str, + score: int | None, + *, + use_score_column: bool, +) -> bool: + """口语短语负向统计语境：评分模式下为 1～2 星；否则为命中负向词（含原「混合」条）。""" + if not stripped: + return False + if use_score_column and score is not None: + return score <= _COMMENT_SCORE_NEG_MAX + hn = any(k in stripped for k in _NEG_CLASS) + return hn + + +def _comment_sentiment_lexicon( + texts: list[str], + scores: list[int | None] | None = None, +) -> dict[str, Any]: """ - 基于预设词表对每条文本做正/负向粗判（非深度学习）；同条同时含正负词时计为「混合」。 - 短语级词频仅在对应语境下统计（条形图用 ``_POS_LEX_HITS`` / ``_NEG_LEX_HITS``）。 + 正/负向粗判（非深度学习）： + + - 若 ``scores`` 与 ``texts`` 等长且**至少有一条非空评分**，则**先按 1～5 星分桶**，再在对应子集内统计 + 正向/负向口语短语（条形图）；无评分或非法评分的行仍按**关键词子串**粗判。 + - 否则：与旧版一致，**仅关键词**划分四象限与短语语境。 """ + use_score_column = bool( + scores is not None + and len(scores) == len(texts) + and any(s is not None for s in scores) + ) pos_only = neg_only = mixed = neutral = 0 corpus_pos_mixed: list[str] = [] corpus_neg_mixed: list[str] = [] - for t in texts: + for i, t in enumerate(texts): s = (t or "").strip() + sc: int | None = None + if use_score_column: + sc = scores[i] if scores is not None and i < len(scores) else None if not s: neutral += 1 continue - hp = any(k in s for k in _POS_CLASS) - hn = any(k in s for k in _NEG_CLASS) - if hp and hn: - mixed += 1 - corpus_pos_mixed.append(s) - corpus_neg_mixed.append(s) - elif hp: + quad = _sentiment_quadrant_for_row(s, sc, use_score_column=use_score_column) + if quad == "pos_only": pos_only += 1 - corpus_pos_mixed.append(s) - elif hn: + elif quad == "neg_only": neg_only += 1 - corpus_neg_mixed.append(s) + elif quad == "mixed": + mixed += 1 else: neutral += 1 + if _include_in_positive_lexeme_corpus(s, sc, use_score_column=use_score_column): + corpus_pos_mixed.append(s) + if _include_in_negative_lexeme_corpus(s, sc, use_score_column=use_score_column): + corpus_neg_mixed.append(s) total = len(texts) pos_lex = _lexeme_hits_in_texts(corpus_pos_mixed, _POS_LEX_HITS) neg_lex = _lexeme_hits_in_texts(corpus_neg_mixed, _NEG_LEX_HITS) + method = "score_then_lexeme" if use_score_column else "keyword_lexicon" + base_note = ( + "「正向短语」与「负向短语」条形图统计的是预设口语片段在**对应语境**下的命中条数，" + "每条每短语最多计 1 次；非分词模型。" + ) + if use_score_column: + scope_extra = ( + "当前批次启用了**评分列**：四象限以星级为主（1～2 星偏负、4～5 星偏正、3 星为中评、空文本为中性）；" + "「正向口语短语」仅在 **4～5 星** 评价条内统计；「负向口语短语」仅在 **1～2 星** 评价条内统计；" + "无评分行仍按关键词子串归入四象限并参与短语语境。" + "条形图不是全文情感或某维度的完整满意度；未收录说法仍可能出现在关注词与语义池。" + ) + else: + scope_extra = ( + "「正向短语」仅在命中正向词表的评价条内统计（含关键词混合条）；" + "「负向短语」仅在命中负向词表的评价条内统计（含关键词混合条）。" + "条形图表示的是「预设短语命中条数」，不是全文情感或某维度（如包装、物流）的完整满意度；" + "若用户用「盒子不错」「没压坏」等未收录说法，仍可能落在关注词「包装」子串与语义池原文中。" + "预设表无法覆盖全部说法（如「一袋就一点点」），须结合语义池原文。" + ) return { - "method": "keyword_lexicon", + "method": method, "text_units": total, "positive_only": pos_only, "negative_only": neg_only, @@ -773,14 +903,7 @@ def _comment_sentiment_lexicon(texts: list[str]) -> dict[str, Any]: "negative_lexicon_sample": list(_NEG_LEX[:10]) + list(_NEG_LEXEME_DETAIL[:5]), "positive_tone_lexeme_hits": pos_lex, "negative_tone_lexeme_hits": neg_lex, - "lexeme_scope_note": ( - "「正向短语」仅在命中正向词表的评价条内统计（含混合条）；" - "「负向短语」仅在命中负向词表的评价条内统计（含混合条）；每条每短语最多计 1 次；" - "统计的是预设口语片段，非分词模型。" - "条形图表示的是「预设短语命中条数」，不是全文情感或某维度（如包装、物流）的完整满意度；" - "若用户用「盒子不错」「没压坏」等未收录说法，仍可能落在关注词「包装」子串与语义池原文中，条形图偏低不代表该维度无人提及。" - "预设表无法覆盖全部说法（如「一袋就一点点」），条形图条数低不代表用户未在别处表述同类不满，须结合语义池原文。" - ), + "lexeme_scope_note": base_note + scope_extra, } @@ -1144,6 +1267,7 @@ def build_scenario_groups_llm_payload( def build_comment_sentiment_llm_payload( texts: list[str], *, + scores: list[int | None] | None = None, attributed_texts: list[str] | None = None, max_samples_positive: int = 16, max_samples_negative: int = 30, @@ -1153,12 +1277,17 @@ def build_comment_sentiment_llm_payload( shuffle_seed: str = "", ) -> dict[str, Any]: """ - 供大模型做正/负向语义归纳：附规则统计、按关键词规则**归类**后的抽样，以及 **sample_reviews_semantic_pool** - （全量去重后的评价句确定性洗牌抽样，供模型结合语境自行判断褒贬）。 + 供大模型做正/负向语义归纳：附规则统计、按**评分优先或关键词**归类后的抽样，以及 **sample_reviews_semantic_pool** + （全量去重后的评价句确定性洗牌抽样，供模型结合语境自行判断褒贬）。 - ``sentiment_bucket_method`` 标明归类依据为子串词表；条形图与 lexicon 计数方式与之一致， - 但正文归纳应以模型对 ``sample_reviews_semantic_pool`` 的整句理解为准。 + ``sentiment_bucket_method``：有有效评分列时为 ``score_then_lexeme``，否则为 ``keyword_substring_heuristic``； + 条形图与 ``comment_sentiment_lexicon`` 计数方式与之一致，正文归纳仍以整句语义为准。 """ + use_score_column = bool( + scores is not None + and len(scores) == len(texts) + and any(s is not None for s in scores) + ) pos_only_texts: list[str] = [] neg_only_texts: list[str] = [] mixed_texts: list[str] = [] @@ -1180,13 +1309,13 @@ def build_comment_sentiment_llm_payload( if disp and disp not in seen_unique: seen_unique.add(disp) all_unique_disp.append(disp) - hp = any(k in s for k in _POS_CLASS) - hn = any(k in s for k in _NEG_CLASS) - if hp and hn: + sc = scores[i] if use_score_column and scores is not None else None + quad = _sentiment_quadrant_for_row(s, sc, use_score_column=use_score_column) + if quad == "mixed": mixed_texts.append(disp) - elif hp: + elif quad == "pos_only": pos_only_texts.append(disp) - elif hn: + elif quad == "neg_only": neg_only_texts.append(disp) def _semantic_pool(seq: list[str], cap: int) -> list[str]: @@ -1225,16 +1354,19 @@ def build_comment_sentiment_llm_payload( break return out - lex = _comment_sentiment_lexicon(texts) + lex = _comment_sentiment_lexicon(texts, scores) pos_h = lex.get("positive_tone_lexeme_hits") or [] neg_h = lex.get("negative_tone_lexeme_hits") or [] pos_h_top = [x for x in pos_h[:12] if isinstance(x, dict)] neg_h_top = [x for x in neg_h[:12] if isinstance(x, dict)] + bucket_method = ( + "score_then_lexeme" if use_score_column else "keyword_substring_heuristic" + ) return { "comment_sentiment_lexicon": lex, "positive_lexeme_hits_top": pos_h_top, "negative_lexeme_hits_top": neg_h_top, - "sentiment_bucket_method": "keyword_substring_heuristic", + "sentiment_bucket_method": bucket_method, "sample_reviews_semantic_pool": semantic_pool, "sample_reviews_positive_biased": _sample(pos_only_texts, max_samples_positive), "sample_reviews_negative_biased": _sample(neg_only_texts, max_samples_negative), @@ -2071,8 +2203,10 @@ def build_competitor_markdown( if n: hits[w] += n - comment_texts = _iter_comment_text_units(comment_rows, merged_rows) - sentiment_lex = _comment_sentiment_lexicon(comment_texts) + comment_texts, comment_scores = _iter_comment_text_units_and_scores( + comment_rows, merged_rows + ) + sentiment_lex = _comment_sentiment_lexicon(comment_texts, comment_scores) scen_counts, scen_n_texts = _comment_scenario_counts( comment_texts, scenario_groups ) @@ -2576,29 +2710,56 @@ def build_competitor_markdown( ] ) - lines.extend( + _sm_score = sentiment_lex.get("method") == "score_then_lexeme" + _sec82_title = ( + "### 8.2 评价正负面粗判（评分优先 + 关键词回退）" + if _sm_score + else "### 8.2 评价正负面粗判（关键词规则）" + ) + _sec82_block: list[str] = [ + "---", + "", + "## 八、消费者反馈与用户画像（按细分类目）", + "", + "### 8.1 方法", + "", + "- **细类划分**：与 **§5 竞品矩阵** 相同，**仅**依据 ``detail_category_path`` 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；**在合并表中但该 SKU 缺 ``detail_category_path`` 或路径无法解析为可读细类的，该评价不进入按细类统计**（与 §5 **同一条排除规则**）。", + "- **正负面粗判（§8.2）**：若评价含有效「评分」列则**先按星级**粗分正负与中评，再在对应子集内统计口语短语；无评分时仍按关键词子串；若任务开启 **llm_comment_sentiment**，可附**大模型对抽样原文的主题归因**，与条形图互补。", + "- **关注词与使用场景（§8.3）**：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 §5 矩阵细类一一对应。", + "", + _sec82_title, + "", + f"- **有效文本条数**：{sentiment_lex.get('text_units', 0)}（与 §8.1 **归因规则**一致）。", + ] + if _sm_score: + _sec82_block.append( + "- **四象限口径**：本批存在有效「评分」时——**1～2 星**计为偏负向，**4～5 星**计为偏正向，**3 星**计为中评，**空文本**计为中性；" + "无评分的条仍按关键词子串划分；「混合」仅在**无评分**且同条兼含正/负关键词时出现。" + ) + _sec82_block.extend( [ - "---", - "", - "## 八、消费者反馈与用户画像（按细分类目）", - "", - "### 8.1 方法", - "", - "- **细类划分**：与 **§5 竞品矩阵** 相同，**仅**依据 ``detail_category_path`` 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；**在合并表中但该 SKU 缺 ``detail_category_path`` 或路径无法解析为可读细类的，该评价不进入按细类统计**（与 §5 **同一条排除规则**）。", - "- **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。", - "- **关注词与使用场景（§8.3）**：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 §5 矩阵细类一一对应。", - "", - "### 8.2 评价正负面粗判（关键词规则）", - "", - f"- **有效文本条数**：{sentiment_lex.get('text_units', 0)}（与 §8.1 **归因规则**一致）。", - f"- **偏正向（仅命中正向词表）**：{sentiment_lex.get('positive_only', 0)} 条；" - f"**偏负向（仅命中负向词表）**：{sentiment_lex.get('negative_only', 0)} 条；" - f"**混合（同条兼含正/负词）**：{sentiment_lex.get('mixed_positive_and_negative', 0)} 条；" - f"**中性或空文本**：{sentiment_lex.get('neutral_or_empty', 0)} 条。", - "- **说明**：词表为方向性粗判，讽刺、省略与错别字会导致误判；正式结论请**人工抽样**阅读原文。", + f"- **偏正向**：{sentiment_lex.get('positive_only', 0)} 条" + + ("（主要为 4～5 星）" if _sm_score else "（仅命中正向词表）") + + "；" + f"**偏负向**：{sentiment_lex.get('negative_only', 0)} 条" + + ("（主要为 1～2 星）" if _sm_score else "（仅命中负向词表）") + + "；" + f"**混合**：{sentiment_lex.get('mixed_positive_and_negative', 0)} 条" + + ("（无评分且同条兼含正/负关键词）" if _sm_score else "（同条兼含正/负词）") + + "；" + f"**中性或空文本**：{sentiment_lex.get('neutral_or_empty', 0)} 条" + + ("（含 3 星中评及无关键词命中）" if _sm_score else "") + + "。", + "- **说明**：" + + ( + "星级与正文可能不一致（如五星长文吐槽）；口语短语条形图仅在对应星级子集内统计；正式结论请**人工抽样**阅读原文。" + if _sm_score + else "词表为方向性粗判，讽刺、省略与错别字会导致误判；正式结论请**人工抽样**阅读原文。" + ), ] ) + lines.extend(_sec82_block) _scope = (sentiment_lex.get("lexeme_scope_note") or "").strip() if _scope: lines.append(f"- **词根统计说明**：{_scope}") @@ -2614,14 +2775,22 @@ def build_competitor_markdown( _embed_chart( run_dir, "chart_positive_lexemes_bar.png", - "正向评价里**最常出现的口语短语**（在偏正向或混合评价条内统计；条形图）", + ( + "正向评价里**最常出现的口语短语**（在 **4～5 星** 评价条内统计；条形图）" + if _sm_score + else "正向评价里**最常出现的口语短语**（在偏正向或混合评价条内统计；条形图）" + ), ) ) lines.extend( _embed_chart( run_dir, "chart_negative_lexemes_bar.png", - "负向评价里**最常出现的口语短语**（在偏负向或混合评价条内统计；条形图）", + ( + "负向评价里**最常出现的口语短语**（在 **1～2 星** 评价条内统计；条形图）" + if _sm_score + else "负向评价里**最常出现的口语短语**（在偏负向或混合评价条内统计；条形图）" + ), ) ) pos_h = sentiment_lex.get("positive_tone_lexeme_hits") or [] @@ -2647,7 +2816,7 @@ def build_competitor_markdown( "", "#### 大模型深入解读（主题归因，与词频统计互补）", "", - "> **说明**：基于与上节**同一套关键词归类规则**抽样的评价原文，由大模型归纳**用户在说什么**（尤其是负向的具体事由），与上列条数、条形图**互补**；引文以原评论为准。", + "> **说明**：基于与上节**同一套评分优先或关键词归类规则**抽样的评价原文，由大模型归纳**用户在说什么**（尤其是负向的具体事由），与上列条数、条形图**互补**；引文以原评论为准。", "", _llm_s, ] @@ -2863,8 +3032,12 @@ def build_competitor_brief( if n: hits[w] += n - comment_texts = _iter_comment_text_units(comment_rows, merged_rows) - comment_sentiment_lexicon = _comment_sentiment_lexicon(comment_texts) + comment_texts, comment_scores = _iter_comment_text_units_and_scores( + comment_rows, merged_rows + ) + comment_sentiment_lexicon = _comment_sentiment_lexicon( + comment_texts, comment_scores + ) scen_counts, scen_n_texts = _comment_scenario_counts( comment_texts, scenario_groups ) diff --git a/backend/pipeline/demos/run_report_llm_chapters_demo.py b/backend/pipeline/demos/run_report_llm_chapters_demo.py index c710de1..071a1e7 100644 --- a/backend/pipeline/demos/run_report_llm_chapters_demo.py +++ b/backend/pipeline/demos/run_report_llm_chapters_demo.py @@ -186,7 +186,9 @@ def main() -> None: chunk_gr = use_chunked_group_summaries_llm(eff_rc) if "sentiment" in only: - comment_units = jcr._iter_comment_text_units(comment_rows, merged) + comment_units, comment_scores = jcr._iter_comment_text_units_and_scores( + comment_rows, merged + ) attr_units = jcr._comment_lines_with_product_context( comment_rows, merged, @@ -199,6 +201,7 @@ def main() -> None: def _sent() -> str: pl = jcr.build_comment_sentiment_llm_payload( comment_units, + scores=comment_scores, attributed_texts=attr_units, semantic_pool_max=40, shuffle_seed=keyword, diff --git a/backend/pipeline/jd/runner.py b/backend/pipeline/jd/runner.py index d99a7a3..668ea47 100644 --- a/backend/pipeline/jd/runner.py +++ b/backend/pipeline/jd/runner.py @@ -369,7 +369,9 @@ 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 = jcr._iter_comment_text_units(comment_rows, merged_rows) + comment_units, comment_scores = jcr._iter_comment_text_units_and_scores( + comment_rows, merged_rows + ) if len(comment_units) >= 2: sentiment_llm_record["attempted"] = True try: @@ -385,6 +387,7 @@ def write_competitor_analysis_for_run_dir( 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, diff --git a/backend/pipeline/llm/generate.py b/backend/pipeline/llm/generate.py index cefecf2..6701edb 100644 --- a/backend/pipeline/llm/generate.py +++ b/backend/pipeline/llm/generate.py @@ -116,8 +116,8 @@ SENTIMENT_LLM_SYSTEM = """你是电商/食品类用户研究助手。输入 JSON - ``comment_sentiment_lexicon``：子串词表统计（与报告条形图**同一计数方式**，**仅作定量参考**；子串命中≠说话人态度）。 - ``positive_lexeme_hits_top`` / ``negative_lexeme_hits_top``：短语级命中摘要（同源）。 -- ``sentiment_bucket_method``：恒为 ``keyword_substring_heuristic``；``sample_reviews_positive_biased`` / ``negative`` / ``mixed_tone`` 是按该词表**机械归类**的抽样，**可能与整句真实褒贬不一致**（例如「软硬适中」曾被误归负向）。 -- **``sample_reviews_semantic_pool``**（若有）：本批评价经去重后的**随机/洗牌抽样**，覆盖未命中任一关键词的句子。**归纳正/负向体验、引用「」短引文时，优先以此池与上述各列表中的原文为准，自行结合语境理解**：转折、对比（如「没那么甜」「软硬适中」）、先抑后扬/先扬后抑整句态度；**不得以子串是否命中负面词来断言该句为抱怨**。 +- ``sentiment_bucket_method``：``score_then_lexeme`` 表示**先按 1～5 星分桶**（无评分行再按关键词）；``keyword_substring_heuristic`` 表示**仅关键词**分桶；与条形图一致。``sample_reviews_positive_biased`` / ``negative`` / ``mixed_tone`` 按该规则**机械归类**的抽样，**可能与整句真实褒贬不一致**（例如「软硬适中」曾被误归负向）。 +- **``sample_reviews_semantic_pool``**（若有）：本批评价经去重后的**随机/洗牌抽样**（来自全部有效条，不限于某一象限）。**归纳正/负向体验、引用「」短引文时，优先以此池与上述各列表中的原文为准，自行结合语境理解**：转折、对比（如「没那么甜」「软硬适中」）、先抑后扬/先扬后抑整句态度；**不得以子串是否命中负面词来断言该句为抱怨**。 每条样本通常以 ``【细类：…｜SKU：…｜品名：…｜店铺：…】`` 开头，表示 **§5 细类、SKU、品名、店铺**；写归纳与「」引文时须能还原「哪家店、哪条 SKU、哪款品名」，或保留前缀，**禁止**无指代地写「用户普遍…」。 diff --git a/backend/pipeline/reporting/charts.py b/backend/pipeline/reporting/charts.py index db60f87..5a662a0 100644 --- a/backend/pipeline/reporting/charts.py +++ b/backend/pipeline/reporting/charts.py @@ -616,7 +616,12 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]: sent = brief.get("comment_sentiment_lexicon") or {} if isinstance(sent, dict): - pie_labs = ["偏正向", "偏负向", "正负混合", "中性/空"] + _score_mode = (sent.get("method") or "") == "score_then_lexeme" + pie_labs = ( + ["偏正向(4～5星)", "偏负向(1～2星)", "关键词混合", "中性/空"] + if _score_mode + else ["偏正向", "偏负向", "正负混合", "中性/空"] + ) pie_vals = [ float(sent.get("positive_only") or 0), float(sent.get("negative_only") or 0), @@ -642,7 +647,11 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]: save_bar_h( plx, pvx, - "正向/混合语境 · 正向口语短语命中条数", + ( + "4～5星语境 · 正向口语短语命中条数" + if _score_mode + else "正向/混合语境 · 正向口语短语命中条数" + ), "chart_positive_lexemes_bar.png", "条数", ) @@ -652,7 +661,11 @@ def generate_report_charts(run_dir: Path, brief: dict[str, Any]) -> list[str]: save_bar_h( nlx, nvx, - "负向/混合语境 · 负向口语短语命中条数", + ( + "1～2星语境 · 负向口语短语命中条数" + if _score_mode + else "负向/混合语境 · 负向口语短语命中条数" + ), "chart_negative_lexemes_bar.png", "条数", ) diff --git a/backend/pipeline/tests/test_competitor_brief.py b/backend/pipeline/tests/test_competitor_brief.py index a966282..c30786d 100644 --- a/backend/pipeline/tests/test_competitor_brief.py +++ b/backend/pipeline/tests/test_competitor_brief.py @@ -60,6 +60,33 @@ class BuildCompetitorBriefTests(SimpleTestCase): self.assertEqual(pl.get("sentiment_bucket_method"), "keyword_substring_heuristic") self.assertGreaterEqual(len(pl["sample_reviews_semantic_pool"]), 1) + def test_comment_sentiment_score_then_lexeme(self) -> None: + root = Path(settings.CRAWLER_JD_ROOT).resolve() + if str(root) not in sys.path: + sys.path.insert(0, str(root)) + import jd_competitor_report as jcr # noqa: WPS433 + + texts = ["很好吃", "太差了", "一般般"] + scores = [5, 1, 3] + lex = jcr._comment_sentiment_lexicon(texts, scores) + self.assertEqual(lex.get("method"), "score_then_lexeme") + self.assertEqual(lex.get("positive_only"), 1) + self.assertEqual(lex.get("negative_only"), 1) + self.assertEqual(lex.get("neutral_or_empty"), 1) + pl = jcr.build_comment_sentiment_llm_payload(texts, scores=scores) + self.assertEqual(pl.get("sentiment_bucket_method"), "score_then_lexeme") + + def test_comment_sentiment_all_scores_missing_falls_back_keyword(self) -> None: + root = Path(settings.CRAWLER_JD_ROOT).resolve() + if str(root) not in sys.path: + sys.path.insert(0, str(root)) + import jd_competitor_report as jcr # noqa: WPS433 + + texts = ["好吃推荐", "差评"] + scores = [None, None] + lex = jcr._comment_sentiment_lexicon(texts, scores) + self.assertEqual(lex.get("method"), "keyword_lexicon") + def test_custom_focus_words_in_report_config(self) -> None: root = Path(settings.CRAWLER_JD_ROOT).resolve() if str(root) not in sys.path: