Google paid trained raters to read 58,000 Reddit comments and mark 27 emotions. The labels reveal two things: every community runs on its own emotional fuel, and humans profoundly disagree about feelings — which should worry anyone shipping an "emotion AI."
Ask five people what emotion a sentence carries and you will often get three answers. GoEmotions is the rare dataset built to expose that fact rather than sand it away: instead of one “ground truth” label per comment, it keeps every rater's judgment. That design choice turns a machine-learning benchmark into a small social-science instrument — one that can measure communities and raters at the same time.
Aggregate the judgments by subreddit and each community develops a legible fingerprint. Support communities — r/SuicideWatch, r/Divorce, r/depression — over-index heavily on caring, and most also over-index on gratitude: people arrive in crisis and thank the ones who show up. Political subs concentrate fear most consistently, with anger and disapproval elevated in some but not all of them. Sports fandoms swing between pride and disappointment — the single most distinctive emotion of r/Mavericks fans is disappointment. None of this is surprising once seen; the point is that it is measurable from twenty-word comments — though what's measured is which emotions raters perceived in a curated, emotion-balanced sample of comments, not the community's underlying emotional mix (see caveats).
Because raters could pick several labels per comment, the data also records which emotions co-occur in a single reading. The result is a map of emotional adjacency: fear ↔ nervousness is by far the tightest pair (18× more likely together than chance), followed by excitement ↔ joy and anger ↔ annoyance. Note what the strong pairs share — they are intensity gradations of the same underlying state, which is precisely what makes them hard to tell apart.
Now the uncomfortable part. For every comment where at least one rater saw a given emotion, how often did the other raters see it too? For gratitude — with its unmistakable surface form, the words thank you — raters split only 56% of the time. For grief, they split 99.1% of the time. Almost never, in 560 comments touching grief, did all raters agree it was there.
This is the epistemic-humility chart every “emotion recognition” product page should carry. A classifier reporting 92% accuracy on grief is being graded against labels that humans themselves reproduce less than half the time. The ceiling isn't the model; it's the concept. GoEmotions, to its credit, ships the disagreement — most benchmarks quietly vote it away.
Source: GoEmotions (Demszky et al., ACL 2020), raw configuration: 211,225 (comment × rater) judgments over 58,011 comments from 483 subreddits, labeled with 27 emotions + neutral. Fingerprints (Fig 1) show selection-rate lift vs. the emotion's global rate, subreddits filtered to ≥500 annotations. Network edges (Fig 2) are within-rater co-selections with Jaccard ≥ threshold shown. Disagreement (Fig 3) is computed over comments with ≥2 raters where ≥1 selected the emotion.
Labels are perceptions of text by raters in one country reading one month (Jan 2019) of Reddit — no time series, no cross-cultural claims. A comment's author may feel none of what readers perceive. GoEmotions' source comments were pre-filtered by length and offensive-content rules and were sampled to oversample rarer emotions (via a pretrained classifier) before being sent to raters — so per-subreddit selection rates reflect what survived that curation and sampling, not a community's raw emotional base rate. Cross-subreddit comparisons (fingerprints, lift) are more robust to this than any single subreddit's absolute rate. Commenter usernames exist in the raw data and are never shown here or in anything published.
Data: Apache 2.0 — cite Demszky et al. (2020), arXiv:2005.00547. Article text and figures: CC BY 4.0.