Scraping Netflix Reviews in the U.S. to Identify High-Demand Genres

Introduction

Why Netflix Reviews Hold the Key to Genre Demand :

With over 80 million+ subscribers in the U.S. alone, Netflix dominates the streaming world. While platform data on views and engagement is guarded, one public window into viewer preferences still exists: user reviews and comments across the Netflix ecosystem.

From Reddit threads to IMDb, Twitter discussions, YouTube reactions, and niche Netflix review sites—audiences openly express their opinions on shows and movies. By web scraping Netflix reviews data, we can tap into these raw sentiments to uncover what truly resonates with viewers.

At Datazivot, we aggregate and analyze these reviews to help:

• Studios spot rising genres
• OTT marketers plan campaigns
• Streaming platforms make smarter recommendations
• Media analysts understand shifts in viewer behavior

Where We Scrape Reviews Related to Netflix U.S.

Netflix doesn’t host direct reviews on-platform, but sentiment lives across:

• Rotten Tomatoes (audience reviews)
• IMDb (user ratings + episode-level reviews)
• Reddit (r/Netflix, r/television)
• YouTube (comments on Netflix trailers)
• Twitter/X (mentions with #NetflixShows, #NetflixMovie)

What Datazivot Scrapes & Analyzes

Sample Review Data Analysis

Key Insights

What U.S. Reviewers Say About Netflix Genres :

1. Dark Comedy & Psychological Thrillers Are Surging

• Beef, The Watcher, and You triggered high comment volumes with polarized sentiment
• “Mind-bending,” “disturbing but brilliant” dominate discussions

2. Teen Dramas Show Viewer Fatigue

• Shows like XO, Kitty or Too Hot To Handle receive backlash on plot depth
• “Recycled,” “shallow,” and “for Gen Z only” were recurring keywords

3. Fantasy & Sci-Fi Have Cult-Like Viewerships

• The Sandman, Stranger Things, Shadow and Bone earned “masterpiece” praise
• High retention rates noted in season-level IMDb reviews

4. Rom-Coms Receive “One-Time Watch” Label

• While light-hearted content like Love in the Villa or Wedding Season get solid starts, few receive “rewatch” mentions

Genre Sentiment Breakdown from U.S. Review Data (2025)

Use Case

Content Team at Netflix Supplier Uses Review Scraping to Pitch New Series :

• Client: Indie U.S. Production House
• Goal: Choose the best genre for 3 limited-budget Netflix pitches

What Datazivot Did:

• 500,000+ Scraped and analyzed reviews from IMDb, Reddit & YouTube
• Created a “Genre Sentiment Heatmap” for the last 50 Netflix Originals
• Identified Dark Comedy + Family Dysfunction as a high-engagement niche

Outcome:

• Pitch: “Suburbia Interrupted” – dark comedy pilot
• Netflix greenlit script development in 90 days
• Trailer launch received 70K+ organic views via genre-based targeting

Why Review Scraping > Viewer Count Alone

Benefits of Netflix U.S. Review Scraping by Datazivot

How Marketers Use Genre Sentiment Intelligence

• Target ad spend by genre-performance zones
• Build landing pages with keyword-aligned social proof
• Create better show tags for discoverability
• Localize creatives for high-engagement regions

Conclusion

Viewers Aren’t Just Watching—They’re Talking :

Netflix might not show you the full viewership story, but the reviews are shouting it.

With Datazivot, studios, OTT platforms, and media teams can:

• Understand what genres are gaining or losing loyalty
• Detect viewer fatigue before launch
• Choose pitches backed by audience emotion
• Optimize trailers, descriptions, and positioning