Case study · Complete
Red Bull YouTube Sentiment Analytics
500 comments, VADER-scored, told a story
Role
Solo — data collection, analysis, report
Period
Apr 2026 · end-term SMA project
The Pitch
Net Sentiment Score of +28.6 pp — roughly 2× the consumer-brand benchmark — with 100% hashtag discipline across a 50-video catalog.
The Problem
Every marketing-ops deck claims "audience sentiment trending positive." Few of them show the math. I wanted to build a sentiment analysis I'd actually trust for a recommendation — starting from the raw comments, with a reproducible pipeline end-to-end.
Red Bull was the right target: a 27.9-million-subscriber channel, consistent content type, and a brand identity that's either working or it isn't. If the methodology holds on a hard case (stunts, POVs, Formula 1) it holds anywhere.
The Approach
Pipeline, six scripts
`scrape_youtube.py` pulls 500 comments across the 5 most-commented recent videos via YouTube Data API v3. `fetch_descriptions.py` enriches video metadata with hashtags from 50 video descriptions via yt-dlp — no API quota burned.
`analysis.py` cleans, scores with VADER (compound ≥ 0.05 → positive, ≤ −0.05 → negative, else neutral), extracts keywords, and categorises complaints. Then `build_excel_dashboard.py`, `build_report.py`, and `build_executive_summary.py` emit the three deliverables: an 8-chart Excel dashboard, a Word report, and a one-page executive PDF.
Sampling choices worth defending
Capped at 100 comments per video × 5 videos = 500 total. A single mega-viral video would dominate if uncapped, and the story I needed was brand-level not video-level.
Hashtag analysis pulled across a wider 50-video catalog — hashtags are brand policy, not audience reaction, so a bigger sample reveals the discipline pattern. That's how the "100% hashtag adoption on every video" finding surfaced.
The findings that matter
Organic keyword frequency: "gives" (35) and "wings" (31) are the top two, ahead of any adrenaline or F1 term. The slogan has genuine unprompted recall.
Hashtag discipline: #RedBull and #GivesYouWiiings appear on 100% of the 50-video catalog. That's unusually strict brand-policy enforcement for an entertainment channel.
The main genuine complaint is viewer anxiety about stunt safety (18 comments), not product taste or price (2). That's a brand-equity signal, not a marketing failure to fix.
Why the numbers are defensible
I chose VADER's official thresholds rather than tuning to taste, deduplicated on comment_id before analysis, and flagged complaint categories by keyword rules that are visible in `analysis.py` — anyone can re-run the pipeline and audit the exact classification rule for any comment.
YouTube Data API v3 quota usage for the full run: under 250 units of the free 10,000/day budget. Reproducible on a free account.
Key Decisions
VADER, not a fine-tuned transformer
A fine-tuned BERT-family model would beat VADER on accuracy by maybe 5–10 percentage points. But VADER is deterministic, auditable, runs locally in seconds, and uses published cut-offs. For a brand-sentiment report going to stakeholders, auditability beats incremental accuracy — "this comment scored negative because its compound score is −0.34" is a defensible claim.
yt-dlp for descriptions, API for comments
The YouTube Data API is rate-limited and expensive when you need 50 video descriptions. yt-dlp parses the public page, no API key, no quota. Using both tools for what each is best at cut the full pipeline quota usage by roughly 80%.
Ship three deliverable formats, not one
Excel dashboard for the analyst, Word report for the write-up, one-page PDF for the executive. A single format leaves one audience under-served. The executive PDF is the link I'd share first to a marketing director.
Metrics
Comments analysed
500
Hashtag catalog
50 videos
Net Sentiment Score
+28.6 pp
≈2× benchmark
Positive / neutral / negative
47 / 34.6 / 18.4
Hashtag discipline
100%
#RedBull + #GivesYouWiiings
API quota used
< 250 units
Live Chart
Rendered from the actual summary.json of the analysis run.
Overall sentiment · 500 comments
Net: +28.6 pp
Industry benchmark for consumer-brand YouTube comments is +10 to +15 pp net sentiment. Red Bull runs roughly 2× that.
Sentiment by video
They Couldn't Look Away
+44 pp
Helicopter Drop Off For This
+38 pp
This Doesn't End
+34 pp
World's CRAZIEST POVs
+ 7 pp
How Quick Are Your Reflexes?
+20 pp
Top organic keywords
The slogan — "gives you wings" — has genuine organic recall.
Numbers rendered live from summary.json of the analysis run. Full dataset and code on GitHub.
Stack
Collection
Analysis
Deliverables