The Question: How Do People Actually Feel About AirPods Pro?
Instead of describing what Sentyment's Market Intelligence module does in the abstract, it's more useful to just show a real run. We pointed it at "airpods pro" — pulling recent comments from Reddit and YouTube — and let it classify sentiment, emotion, and the specific quotes behind each score. This is the exact query shown live on the Sentyment homepage; here's the full breakdown of what came back.
The Headline Number
A net score alone is a headline, not an insight. The useful part is what is actually driving each bucket — which is where the comment-level evidence comes in.
The Comments Behind the Score
Four representative comments pulled directly from the sample, classified individually:
- "Noise cancelling is genuinely unreal, worth every penny." — r/headphones, ↑1.2k (Positive)
- "Switching between my devices is seamless now." — r/ios, ↑612 (Positive)
- "Setup was fine, nothing special but it works." — r/apple, ↑340 (Neutral)
- "Mine started crackling after 3 months, disappointed." — r/AirPods, ↑880 (Negative)
This is the part a raw percentage cannot give you: the noise cancelling and cross-device switching experience are the two threads driving the positive share, while a recurring "crackling after a few months" complaint is the specific, actionable signal behind the negative 15% — not vague dissatisfaction, a concrete quality issue worth tracking.
Beyond Positive/Negative: The Emotion Layer
Sentyment classifies emotion, not just polarity, across five buckets. For this query, on a 250-comment sample:
- Joy — 34%
- Trust — 27%
- Neutral — 24%
- Concern — 9%
- Anger — 6%
The net read: this conversation leans joy. Trust (comments about reliability and consistency) outweighs concern by a wide margin, and anger — the emotion most associated with a real reputational problem — sits at just 6%. That distinction matters: a product can have real, specific complaints (the crackling issue above) without the overall conversation reading as angry or hostile.
What This Took
- Query: one URL/keyword, no manual scraping or spreadsheet work
- Turnaround: under 10 seconds for the full 250-comment pull and classification
- Cost: 1 credit on the Free plan for a Reddit or YouTube query
- Output: net score, emotion spread, and cited comments — exportable as CSV
Why Show the Raw Output
Most product pages describe what a tool does. It's more convincing to just show what comes back from a real query, including the parts that are mildly inconvenient — like a real, specific hardware complaint sitting inside an otherwise positive conversation. That's the point: evidence-backed sentiment doesn't round off toward a clean story, it shows you the actual texture of the conversation.
Run Your Own
This exact workflow is available on the Free plan — 20 credits a month, no card required. Point it at your own brand, a competitor, or a product launch and see the same breakdown: net score, emotion spread, and the comments behind it.