So this all started when I was refactoring my media streaming dashboard last Tuesday. Wanted to optimize recommendation algorithms, but every forum screamed “UNDERSTAND YOUR AUDIENCE FIRST.” Fine. Decided to dig into what actual women prefer. Not assumptions, real data.
The Messy Setup
First, scraped Reddit relationship advice threads going back 18 months using Python scripts – yeah, the messy way. Filtered keywords like “watch together” or “private viewing habits.” Thousands of comments. Nearly crashed my old Macbook twice doing sentiment analysis.
The Dirty Work
Had to manually clean so much junk data:
- Deleted all troll replies (like “hurr durr she just needs a real man”)
- Filtered corporate survey spam
- Trashed fake “studies” pushing subscription sites
- Separated actual shared experiences from hypotheticals
Saw more purple dildo ads than actual insights. Felt like shoveling digital manure.
Pattern Hunting Hell
Finally clustered recurring themes using NLTK. Three patterns kept slapping me in the face:
- Plot Twist Over Shock Value: Women mentioning skipping straight to story-heavy scenes. One user wrote: “If there’s no buildup or tension, it’s just… awkward plumbing.”
- Amateur & DIY Vibes: Overwhelming preference for “this could be us” content over studio glitz. Real lighting, actual laughter, zero wax jobs.
- Female Lens Everything: Behind-camera male gaze got roasted constantly. Comments like “Her face looks pained? Pass” appeared 43 times in the dataset. Directors matter.
Why Trust My Results?
Honestly? I almost gave up halfway. Then my wife’s book club used my workstation for Zoom. They saw my chaotic Jupyter notebook tabs and screenshots. Got ambushed during cookie break.
Lisa said: “Oh honey we skipped this talk last month when Barb’s mic was live.” Jenny added: “Plot? Obviously. That vampire office romance series? Pure gold.” Maggie chimed in: “Make it look real or don’t bother.”
Suddenly had 12 real women giving unsolicited peer review. Validation by baked goods and brutal honesty.
The Bittersweet Payoff
Rejigged my recommendation engine using these filters. Engagement shot up 37% with female testers. But here’s the kicker: three streaming services I pitched this to loved the findings. Hated that “women” was used as a monolith in the data viz.
Got ghosted after saying “Yeah nah I won’t remove the ‘disclaimer’ slide saying preferences vary wildly.” Corporate suits want simple lies. I keep messy truths.
Anyway, coding this almost bricked my laptop. Found out Python should never parse Reddit comment threads while your roommate torrents entire Marvel movies. Lesson learned.
