Fluidnotes: Gender & Fragrance

Data Visualization | d3.js | Webscraping | Machine Learning

Thesis for Parsons MS Data Visualization
Guided by Sam Lavigne & Matias Pina

Live Site

Github

What smells “feminine” or “masculine” isn’t chemistry — it’s about culture, time, and storytelling. They change — just like our ideas of gender. My thesis, Fluidnotes, maps over 25,000 perfumes and 100 years of gender history to explore how fragrance culture reflects - and sometimes challenges - our evolving understanding of gender.

Using machine learning and computer vision tools, this project was able to analyze 250+ vintage advertisements, map fragrance notes into categories, and connect visuals to scent compositions. While the dataset does not represent the entirety of the global perfume market, it covers a broad span to reflect the Western industry’s dominant trends.

4 Minutes Project Recap

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The project brings together three interactive components:

(1) a scrollable timeline that pairs over 100 years of fragrance release data with key gender milestones—from women’s suﬀrage to the rise of unisex marketing;

(2) a note migration graph that shows how specific scents like musk or rose have shifted between gender categories; and

(3) an analysis of 250 perfume ads, revealing how gender is portrayed, expressed, and sometimes subverted in visual storytelling.

DATA COLLECTION AND PROCESSING

Data	Description	Access
Fragrantica Dataset	Over 24,000 entries with notes, ratings, accords, perfumers, etc.	Link
Parfumo Year of Release	Counts of discontinued/current perfumes; “popular fragrance of the era”.	Link
Fragrantica Notes	Scraped notes + images for mapping.	Link
Ad*Access (Duke)	7,000+ US/Canada print ads (1911–1955), beauty & hygiene.	Link
All Ads	A collection of vintage archives and recent social media posts.	Link
Advertising Archives UK	The advertisements are arranged by decade and can be searched by product. Many American advertisements are included.	Link
GPT Vision	Automate visual analysis of vintage perfume ads using ChatGPT Vision - test.py file in github	Link
Key Gender Milestones	Compiled list of key gender milestones from 1900 to 2025 by aggregating information from multiple web sources, including historical archives, academic databases, and cultural timelines.	Link

I used webscraping to gather fragrance, advertising, and historical datasets, then cleaned and standardized the data to resolve inconsistencies. With ChatGPT Vision, I automated visual analysis of vintage perfume ads, and applied machine learning to classify and detect patterns. These tools formed an integrated pipeline for creating a clean, research-ready dataset.

BEHIND THE SCENES

“Time” is the center of this visualization as I’m exploring the change and how the time influenced the fragrance industry. The whole page is a scrollytelling experience where you scroll through the years layered at the bottom.

To learn more about my process and discoveries, visit the github repo and read my thesis paper!