FragDBnet/fragrance-database

FragDB v5.3 — Fragrance Database (Multilingual, Sample)

The most comprehensive structured fragrance database available. This is a free sample containing 10 fragrances with related brands, perfumers, notes, accords, and a complete translation vocabulary.

What's New in v5.3

• 23 languages — all labels, note names, accords, countries, statuses translated
• translations.csv — new vocabulary file (34 entries) for gender and voting labels
• Compact notes pyramid — note_id,opacity,weight (name/icon via notes.csv JOIN)
• 2,517 notes — each name variant has its own ID with translations
• Perfumer transliterations expanded to 6 languages — added Chinese, Korean, Arabic
• Gender & voting fields use translation IDs for multilingual support

Dataset Description

File	Records	Fields	Description
fragrances.csv	10	30	Top-rated fragrances
brands.csv	10	54	Brand profiles + 22 lang translations
perfumers.csv	10	39	Perfumer profiles + 22 lang + 6 name translit
notes.csv	10	55	Fragrance notes + 22 lang translations
accords.csv	10	27	Accords + 22 lang translations
translations.csv	34	25	Gender & voting vocabulary (full)
comments_sample.parquet	25	8	User reviews preview (parquet)
news_sample.parquet	20	16	Editorial articles preview (parquet)
news_comments_sample.parquet	20	9	News comments preview (parquet)
SPEC.md	—	—	Parquet schema documentation

Companion Parquet Datasets — User Reviews, News, and Community Comments

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.

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

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

The parquet datasets ship with all paid tiers except the $200 Core:

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

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.

Use Cases

CSV Core (all tiers):

• E-commerce — Enrich product listings with detailed fragrance data, notes, accords
• Mobile Apps — Build fragrance collection managers, scent discovery apps, perfume catalog apps
• Data Analysis — Analyze fragrance industry trends by brand, country, perfumer, year
• Recommendations — Content-based or collaborative filtering systems using accord/note vectors
• Multilingual UIs — Localized perfume catalogs in 23 languages out of the box
• Knowledge Graphs — Brand → Perfumer → Fragrance → Notes → Accords graph construction
• Market Research — Country-of-origin analysis, parent company portfolios, perfumer productivity stats

Parquet Datasets ($400+ tiers):

• NLP & Sentiment Analysis — Train models on 4.6M multilingual fragrance reviews
• Recommender Systems — Hybrid models combining CSV structure with review text similarity
• Language Models — Domain-specific corpus for fragrance/perfumery LLM fine-tuning
• Review Classification — Identify positive/negative reviews, fake review detection
• Trend Detection — News article timeline analysis, emerging fragrance trends
• Author Networks — Identify influential reviewers, perfumery journalists, community leaders
• Content-Based Discovery — "Articles about this perfume" — JOIN news.related_pids with fragrances.pid
• Community Analytics — Reply networks, engagement metrics on editorial content
• Cross-Language Studies — Compare review sentiment across 23 languages for the same fragrance
• Search Engines — Full-text search across reviews, articles, and structured metadata
• Knowledge Extraction — Mine 24K editorial articles for perfume facts, launch dates, perfumer interviews

Full Database

	Sample	Full Database
Fragrances	10	130,086
Brands	10	7,776
Perfumers	10	2,960
Notes	10	2,517
Accords	10	92
Translations	34	34
Languages	23	23
Total Records	84	143,465

Quick Start

import pandas as pd

fragrances = pd.read_csv('fragrances.csv', sep='|')
brands = pd.read_csv('brands.csv', sep='|')
notes = pd.read_csv('notes.csv', sep='|')
translations = pd.read_csv('translations.csv', sep='|')

# Join and translate
fragrances['brand_id'] = fragrances['brand'].str.split(';').str[1]
df = fragrances.merge(brands, left_on='brand_id', right_on='id', suffixes=('', '_brand'))
trans = translations.set_index('id')
df['gender_ru'] = df['gender'].map(lambda x: trans.loc[x, 'ru'] if x in trans.index else x)
print(df[['name', 'name_brand', 'country_ru', 'gender_ru']])

File Format

• Format: CSV (pipe | delimited)
• Encoding: UTF-8
• Quote Character: " (double quote)

Links

• Full Database: fragdb.net
• GitHub: github.com/FragDB/fragrance-database
• Kaggle: kaggle.com/datasets/eriklindqvist/fragdb-fragrance-database

License

This sample is released under the CC BY-NC 4.0 License. Free for non-commercial use with attribution.

Citation

@dataset{fragdb2026,
  title={FragDB Fragrantica Fragrance Database},
  author={FragDB},
  year={2026},
  version={5.3},
  url={https://fragdb.net},
  note={Multilingual dataset with 6 files, 23 languages}
}