Amazon Keyword Library Builder

Amazon Keyword Library Builder

Overview

End-to-end pipeline that turns raw mined keyword data into a production-ready keyword library.

Design principle

• Mining stays upstream (SIF reverse lookup, ABA, search-suggestion expansion, SellerSprite, reviews). This skill does not re-mine.
• This skill only consumes the mined results and applies tool-score-first classification + positive/negative split + root analysis + negative-phrase extraction.
• Main tagged table stays simple. Extra sheets deliver the operational value (肯定/否定词库, 词根词频, 否定词根建议).

Core outputs

1. Simple tagged master table
2. 肯定词库
3. 否定词库
4. 词根词频 (positive + negative, 1-gram + 2-gram)
5. 否定词根建议 (high / medium priority phrase negatives + exact negatives)

When to use

• User already has a SIF export, ABA list, SellerSprite keyword list, or mixed mining result
• User finished keyword mining and needs structured tagging + negative roots
• User wants a complete library for Title / Bullets / Backend / PPC Exact-Phrase + Negative

Instructions

0. Required context (ask if missing)

1. Product core

   • Product name, key features, target audience, main scenarios
   • Own brand (if any)
   • 3–8 competitor ASINs (strongly preferred)

2. Input data

   • File or table from SIF / ABA / SellerSprite / Jungle Scout / Search Term Report / mixed
   • Confirm which columns are present (see mapping below)

3. Goal

   • Default = full multi-sheet library
   • Or listing-only / PPC-only / negative-focused

Do not invent relevancy or volume. If critical columns are absent, note low confidence and still produce best-effort tags.

1. Input field mapping (SIF-first, then others)

Prefer these columns when present (Chinese or English names):

Sif 转化表现标记 → 相关度 bias

• 购买转化词 / 高质量转化词 / 稳定转化词 → strong positive bias
• 转化流失词 → keep, mark for optimization
• 无效曝光词 → strong negative candidate

Preserve original tool values in Notes or extra columns. Never overwrite them.

2. Clean

• Deduplicate exact and near-duplicates
• Drop zero-volume nonsense or clear wrong-category terms
• Keep misspellings and variants (route to Backend later)
• Keep all original tool scores and labels

3. Multi-dimensional tagging (tool scores first)

3.1 相关度

Order of preference:

1. Sif / SellerSprite occupancy or 转化表现标记
2. Explicit relevancy grade / score
3. ABA Top-3 ASIN similarity
4. Product-context judgment (mark low confidence)

Grades: 高相关 / 中相关 / 低相关 / 不相关

3.2 流量层级

Prefer ABA SFR:

• 高流量 ≈ SFR ≤ 50k (tighten in ultra-competitive categories)
• 中流量 ≈ 50k–200k
• 低流量/长尾 ≈ > 200k

Else use 周搜索量 / Monthly Searches with relative ranking inside the list.

3.3 词性

Use root frequency first when available, then product context.

Main tags: 核心产品词, 类目大词, 属性词, 功能卖点词, 场景词, 人群词, 规格参数词, 长尾修饰词, 自有品牌词, 竞品品牌词, 关联互补词, 礼品词, 否定词

See references/taxonomy-details.md.

3.4 优先级

• P1 = 高相关 + 中高流量 + conversion signal → Title + Exact PPC
• P2 = 中相关 or high-relevancy mid-volume → Bullets / Phrase
• P3 = long-tail high-relevancy or experimental → Backend / Broad

3.5 建议位置

Title / 五点 / 描述A+ / 后台 / Exact PPC / Phrase / Broad / Negative

See references/placement-mapping.md.

4. Positive / Negative split

• 肯定词库: 高相关 + 中相关 (+ any strong purchase-conversion terms that still fit the product)
• 否定词库: 低相关 + 不相关 + 无效曝光词 + clear semantic mismatches

This split is the input to root analysis and negative extraction.

5. Root frequency analysis (1-gram + 2-gram)

Run separately on 肯定词库 and 否定词库.

Algorithm (see references/root-and-negative-logic.md):

1. Normalize (lower-case, trim)
2. Tokenize on spaces
3. Generate 1-grams and ordered 2-grams
4. Count frequency (optionally search-volume weighted)
5. Filter pure stop combinations carefully; keep intent phrases (for women, with pockets, plus size)
6. Output sorted tables labeled 1-gram / 2-gram

6. Automated negative root & 2-gram phrase extraction

Core metric:

negation_strength = neg_count / (pos_count + 1)

(or volume-weighted)

Decision order:

1. Force-list hit → high-priority phrase negative
2. Protect-list hit → never auto-promote
3. strength ≥ 3 and support ≥ 3 (or 5) → high-priority
4. strength 1.5–3 → medium, human confirm
5. Semantic conflict (wrong audience, opposite function, low intent) → boost
6. Prefer 2-gram over 1-gram when both qualify

Output three layers:

• High-priority Negative Phrase
• Medium-priority (review)
• Exact-negative full keywords

Update force/protect lists per product category before extraction. Templates in references/root-and-negative-logic.md.

7. Output Excel structure (required)

Sheet 1 — Tagged Keywords (simple core, never drop)

| Keyword | 词性 | 流量层级 | 相关度 | 优先级 | 建议位置 | Search Volume / ABA Rank | Notes | Source |

Sort by 优先级 → 流量层级 → 相关度.

Sheet 2 — Summary

• Counts by 词性 / 相关度 / 优先级
• Title seed list
• Backend candidates
• Confidence / logic notes for top terms

Sheet 3 — 肯定词库 Positive keywords + original useful tool fields when available.

Sheet 4 — 否定词库 Negative keywords + original useful tool fields.

Sheet 5 — 词根词频_肯定 1-gram + 2-gram from positive set.

Sheet 6 — 词根词频_否定 1-gram + 2-gram from negative set.

Sheet 7 — 否定词根建议 Ranked phrase negatives with strength, support, examples, priority, 1-gram/2-gram flag + exact-negative full list (or reference).

If user only wants the simple table, deliver Sheet 1 + Summary. Default is the full library.

8. Special rules

• Competitor brands → PPC only, never organic Title/Backend
• Own brand → careful, usually not in Backend search terms
• New ASIN / thin data → bias to high-relevancy long-tail first
• Tool score conflicts → prefer Sif/SellerSprite conversion or occupancy for relevancy + ABA SFR for volume; record conflict in Notes
• Chinese + English keywords supported; keep tag language consistent with user

9. Iteration

After delivery:

• Accept user corrections on borderline rows
• Re-run root analysis and negative extraction if the positive/negative split changes
• Optionally draft Title / Bullets from P1+P2
• Export Backend-only list (byte-aware ≤249 bytes)

References

• references/taxonomy-details.md — 词性 examples
• references/placement-mapping.md — placement rules
• references/tool-score-mapping.md — ABA / SellerSprite / Jungle Scout / Sif mapping
• references/root-and-negative-logic.md — full 1-gram/2-gram algorithm, negation strength, force/protect lists, decision rules
• assets/keyword-tagging-template.xlsx — base template (extend with extra sheets)

Integration note for upstream mining

This skill expects already mined keyword rows. Typical upstream sources:

• SIF ASIN reverse lookup (转化表现标记, 流量特征, 周搜索量, 自然排名…)
• ABA Brand Analytics (SFR, click share, conversion share)
• SellerSprite / Jungle Scout keyword exports
• Search Term Report converters
• Search-suggestion expansion results (T1/T2/T3 style)

Do not re-implement mining inside this skill. Accept the data, map columns, tag, split, analyze roots, extract negatives, and deliver the library.