Boss vs. Bloom: Uncovering Gender Bias in Fragrance Descriptions with Python

2026-05-21 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

An analysis of ~700 Sephora UK fragrance listings reveals significant gender-based differences in marketing language and pricing. Using Python for web scraping, lemmatization with spaCy's en_core_web_sm model, and document-term matrices, the study found women's fragrances averaged £2.13/ml compared to men's £1.45/ml. Text analysis showed women's product descriptions frequently use words like "floral" (0.58%), "rose", and "delicate", evoking softness and sweetness. Conversely, men's descriptions emphasize terms such as "intense" (0.37%), "woody", "alpha", and "boss", aligning with traditional masculine connotations. The project's code and scraped data are publicly available on GitHub.

Key takeaway

For Data Scientists or NLP Engineers investigating market biases, this analysis demonstrates a practical pipeline for uncovering gender-based disparities in product descriptions and pricing. You can adapt this methodology, including web scraping with "requests" and "BeautifulSoup" and spaCy for text processing, to analyze other product categories or services. Consider replicating the approach to identify subtle linguistic cues that may reinforce stereotypes or reveal pricing inequities in your own domain.

Key insights

Fragrance marketing language and pricing systematically reinforce traditional gender stereotypes.

Principles

• Marketing language reflects and reinforces gender stereotypes.
• Pricing can show gender-based disparities.
• NLP can quantify subtle linguistic biases.

Method

The analysis pipeline involves two-stage web scraping, lemmatization with spaCy, building document-term matrices, and computing relative frequency deltas to identify gender-specific word usage.

In practice

• Scrape e-commerce sites for product data.
• Use spaCy for efficient text lemmatization.
• Compare word frequencies to detect linguistic bias.

Topics

• Gender Bias
• Natural Language Processing
• Web Scraping
• Fragrance Marketing
• spaCy
• Document-Term Matrix

Code references

• colinvn/perfumes-by-gender

Best for: Data Scientist, NLP Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.