<b>When 'love' stopped meaning love</b>

<b>When 'love' stopped meaning love</b>

It was a routine tune-up when an apparel brand's sentiment model started overcounting positives. Their numbers said 74%; the comms team's gut said something colder. The gut was right.

The model had been trained two years earlier, when a certain skull emoji and 'I'm dead' read as enthusiasm. The audience had drifted. Now those same tokens increasingly framed sarcasm — 'this restock, I'm dead' meant frustration, not delight. The model was scoring eye-rolls as applause.

They hand-labeled 600 recent mentions and found the classifier was 19 points too optimistic on the youngest cohort. Recalibrated, true sentiment was 55%, not 74% — and the gap explained a campaign that 'tested well' but sold poorly.

The takeaway: language drifts faster than your model. A sentiment classifier left untrained for two years isn't measuring your audience — it's measuring a memory of them. Re-label quarterly.