How the crowd actually feels about 21 consumer products, measured from 38.6B interactions across X, Reddit, TikTok, YouTube, Instagram, and 10,000+ news sources. Not a survey: the observed mood of engagement itself.
Share of each product's interactions that land on positive-sentiment posts (24-48h window). The bar is the crowd voting with its attention.
Ranked by non-neutral share: high positive AND high negative at the same time. Divisive is not bad; it is engagement you can measure both sides of.
| Product | Positive | Negative | Neutral | Non-neutral |
|---|---|---|---|---|
| McDonald's Corporation | 84% | 4% | 12% | 88% |
| Starbucks Corporation | 51% | 9% | 40% | 60% |
| Uber Technologies, Inc. | 42% | 15% | 43% | 57% |
| DoorDash, Inc. | 7% | 49% | 44% | 56% |
| Macbook | 33% | 23% | 44% | 56% |
| Temu | 51% | 4% | 45% | 55% |
| XBox | 40% | 12% | 48% | 52% |
| NIKE, Inc. | 47% | 5% | 49% | 51% |
| OpenAI | 38% | 12% | 50% | 50% |
| Shein | 32% | 17% | 51% | 49% |
Per-network sentiment score (0-100). The Spread column is the gap between a product's warmest and coldest platform: brand teams read rows, allocators read the column.
| Name | X | TikTok | YouTube | Spread | ||
|---|---|---|---|---|---|---|
| DoorDash, Inc. | 78 | 72 | 13 | 62 | – | 65 |
| Uber Technologies, Inc. | 31 | 70 | 82 | 38 | 93 | 62 |
| Macbook | 55 | 82 | 78 | 49 | 100 | 51 |
| Airbnb, Inc. | 51 | 77 | 75 | 74 | 96 | 45 |
| McDonald's Corporation | 90 | 56 | 100 | 100 | – | 44 |
| OpenAI | 84 | 79 | 75 | 79 | 43 | 41 |
| iPhone | 86 | 79 | 74 | 76 | 48 | 38 |
| Tesla, Inc. | 86 | 68 | 51 | 72 | 64 | 35 |
| Rivian | 80 | 79 | 81 | 55 | 90 | 35 |
| Spotify Technology S.A. | 80 | 83 | 64 | 72 | 94 | 30 |
| NIKE, Inc. | 81 | 82 | 94 | 79 | 64 | 30 |
| Playstation | 70 | 73 | 81 | 78 | 98 | 28 |
| Robinhood Markets, Inc. | 86 | 91 | 73 | 79 | 100 | 27 |
| Shein | 92 | 91 | 70 | 84 | 97 | 27 |
| Temu | 73 | 74 | 95 | 93 | 98 | 25 |
| Airpods | 54 | 78 | 69 | 60 | 56 | 24 |
| Starbucks Corporation | 70 | 66 | 88 | 89 | 90 | 24 |
| XBox | 77 | 85 | 77 | 82 | 100 | 23 |
| Netflix, Inc. | 87 | 77 | 68 | 70 | – | 19 |
| Adidas | 77 | 89 | 81 | 84 | 70 | 19 |
| Red Bull | 75 | – | – | – | – | 0 |
Sentiment here is engagement-weighted: the share of interactions landing on posts classified positive, negative, or neutral. It answers "what mood is the crowd amplifying," not "what do owners think." A product can be beloved by its users and still bleed negative engagement when one video goes viral. That distinction is exactly why the per-network split matters: the platform where the mood breaks first is usually the story.
This page is a one-day snapshot. The commercial feed is the same measurement as a continuous hourly time series, per product, per network, with history.
Every product in this study: interactions, sentiment shares, per-network scores. No registration.
The .md file is a companion brief for AI assistants: schema, methodology, and validation anchors. Drop it into Claude or ChatGPT next to the data and your model starts from the right place.
The commercial feed is this exact measurement as continuous hourly time series with history, for any product, brand, or ticker universe. Most teams have it in a dashboard the same week.