Expand README: prominent Companion Parquet section with detailed descriptions, tier table, use cases, SEO

README.md

@@ -561,17 +561,81 @@ The most comprehensive structured fragrance database available. This is a **free
 | `news_comments_sample.parquet` | 20 | 9 | News comments preview (parquet) |
 | `SPEC.md` | — | — | Parquet schema documentation |
 
-### Companion Parquet Datasets (Full tier only)
+## Companion Parquet Datasets — User Reviews, News, and Community Comments
 
-The full parquet datasets (not in this sample) ship with all paid tiers except the $200 Core:
+FragDB ships with **three Apache Parquet datasets** containing **4.9 million rows** of user-generated content and editorial coverage — the largest publicly-organized corpus of fragrance reviews and perfumery journalism. Use them for NLP, sentiment analysis, recommendation systems, market research, or training language models on fragrance-specific text.
 
-| File | Rows | Description |
-|------|------|-------------|
-| `comments.parquet` | 4,643,851 | User reviews in 23 languages |
-| `news.parquet` | 24,440 | Editorial articles, 2008-2026 |
-| `news_comments.parquet` | 263,798 | Threaded news comments |
+**Keywords:** fragrance reviews · perfume reviews · multilingual UGC corpus · NLP training data · fragrance sentiment · perfumery journalism · perfume recommendation · scent recommendation · review classification · entity linking · knowledge graph · fragrance industry news · perfume articles
 
-See `SPEC.md` for full schema. Pricing: https://fragdb.net/#pricing
+### `comments.parquet` — 4.6 Million User Reviews in 23 Languages
+
+The world's largest collection of structured fragrance reviews. Every entry includes the perfume ID (joinable with `fragrances.csv`), author username, posting date, full review text, avatar URL, and language code.
+
+- **4,643,851 user reviews** spanning every major perfume on Fragrantica
+- **23 languages** — English (1.69M), Russian, Portuguese, Spanish, Korean, Turkish, Japanese, Polish, Italian, Hungarian, Serbian, Swedish, German, Hebrew, Ukrainian, French, Arabic, Greek, Czech, Chinese, Romanian, Mongolian, Dutch
+- **Coverage:** 70.6% of all fragrances have at least one review (93,305 of 132,160 PIDs)
+- **Deterministic global primary key** — stable comment IDs survive re-scrapes
+- **Zero duplicate rows**, **zero foreign key orphans** against `fragrances.csv.pid`
+- **Independent UGC per language** — genuine localized content, not machine translation
+- **8 fields:** `pid`, `lang`, `comment_id`, `author`, `date`, `text`, `avatar_url`, `gradient_class`
+- **PyArrow large_string format** — combined corpus exceeds 32-bit string offset limit
+
+**Use cases:** sentiment analysis · review classification · recommendation systems · perfume similarity from text · language detection benchmark · multilingual NLP training corpus · fragrance market research · author network analysis · trend detection by language
+
+### `news.parquet` — 24,440 Editorial Articles (2008–2026)
+
+Two decades of professional fragrance journalism from Fragrantica's editorial team. Every article includes title, author, full text (plain + HTML), category, related perfumes/brands/perfumers, publication date, and main image.
+
+- **24,440 editorial articles** from 2008 to 2026 — complete public archive
+- **30+ categories** — New Fragrances (34.9%), Fragrance Reviews (22.8%), Niche Perfumery (10.4%), Designer Brands, Interviews, History, Industry News
+- **Bilingual storage** — `text` (plain) for NLP, `text_html` (markup preserved) for rich display
+- **Linked entities** — `related_pids[]`, `related_brands[]`, `related_perfumers[]` as JSON arrays
+- **0% orphans** over 119,662 PID references
+- **63.1% archived legacy, 36.9% modern** fully-dated articles
+- **16 fields:** `nid`, `title`, `category`, `author`, `url`, `is_archived`, `date_unix`, `description`, `text`, `text_html`, `main_image`, `article_images`, `related_pids`, `related_brands`, `related_perfumers`, `comments_count`
+
+**Use cases:** content recommendation · article search engine · perfume knowledge graph · trend analysis · author influence study · entity linking · timeline analysis · industry research · niche perfumery research
+
+### `news_comments.parquet` — 263,798 Threaded Community Comments
+
+Community discussions attached to editorial articles, with threading support for replies. Joinable with `news.parquet` via `nid`.
+
+- **263,798 threaded comments** across **21,820 articles** (89.3% of news articles have ≥1 comment)
+- **4.9% reply rate** — threaded conversations with reply detection
+- **100% populated timestamps**
+- **9 fields:** `nid`, `comment_id`, `is_reply`, `author`, `date`, `date_unix`, `text`, `avatar_url`, `gradient`
+
+**Use cases:** community engagement analysis · threaded discussion mining · reply network construction · comment sentiment · author activity profiles
+
+### Tier Availability
+
+| Tier | CSV Core | Parquet Datasets |
+|------|----------|------------------|
+| **$200 One-Time Core** | ✅ | ❌ |
+| **$400 One-Time Full Database** | ✅ | ✅ |
+| **Annual Subscription** | ✅ | ✅ (always latest) |
+| **Lifetime Access** | ✅ | ✅ (always latest) |
+
+See https://fragdb.net/#pricing for complete tier comparison.
+
+### Quick Start — Parquet
+
+```python
+import pyarrow.parquet as pq
+import pandas as pd
+import json
+
+reviews = pq.read_table('comments.parquet').to_pandas()
+fragrances = pd.read_csv('fragrances.csv', sep='|')
+reviews_with_meta = reviews.merge(fragrances, on='pid', how='left')
+
+news = pq.read_table('news.parquet').to_pandas()
+news['related_pids_list'] = news['related_pids'].apply(json.loads)
+
+news_comments = pq.read_table('news_comments.parquet').to_pandas()
+```
+
+Full schema in [`SPEC.md`](SPEC.md).
 
 ### Full Database