sentiment agent misclassified angry ticket as neutral: threshold tuning

TL;DR

Support anger doesn't look like training-data anger: it's polite phrasing over fury ('I would appreciate it if someone could finally...'), which scores neutral on generic models. The fix is threshold tuning on your own tickets: label real examples, find where angry actually scores, and set the cutoff there. Generic thresholds are calibrated on movie reviews, not support queues.

The query

sentiment agent misclassified angry ticket as neutral: threshold tuning

Use this when

• Angry tickets get classified as neutral
• Sentiment scores cluster in the middle
• Retraining the model didn't fix it

Not for

• Human sentiment labeling guidelines
• Sarcasm detection research
• Sentiment for non-support text

Steps

1. Label real tickets, not synthetic ones

Pull two hundred tickets and label them angry, neutral, happy yourself. Include the polite-furious ones. This labeled set is the ground truth every step below uses.

Expected output: a labeled set of real tickets with genuine support anger represented.

2. Score the labeled set and plot the distribution

Run your sentiment model over the labeled tickets and look at where angry tickets actually score. You'll usually find them scoring 0.4 to 0.6, squarely in the default 'neutral' band.

Expected output: the score distribution showing angry tickets landing in neutral territory.

3. Move the thresholds to fit your data

Set the angry threshold where your angry tickets score, not where the docs suggest. If angry tickets score 0.45, then 0.45 is angry. Validate on a held-out set so you're not overfitting.

Expected output: thresholds tuned on your tickets with held-out validation.

4. Add support-specific signals

Thresholds alone are blunt. Add features the generic model misses: exclamation density, words like 'unacceptable' and 'escalate', ticket reopen counts, and time-since-last-reply. Anger in support is behavioral, not just lexical.

Expected output: auxiliary signals catching what the base score misses.

Variant phrasings

sentiment analysis wrong on support tickets

Steps 1 through 3: label, plot, retune.

angry customer detected as neutral

Step 4's behavioral signals for the polite-furious cases.

Why it happens

Generic sentiment models learn anger from dramatic text: rants, insults, caps lock. Support anger is restrained by the medium: customers write to get help, so they stay civil while furious. The model sees civility and scores neutral. The threshold is wrong because the calibration data never contained a politely furious enterprise customer.

Edge cases

• Sarcasm ('great, thanks for nothing') still fools most models. Flag it as a known gap.
• Thresholds drift as ticket mix changes. Re-tune quarterly, not once.
• Never auto-punish on sentiment alone. Use it for prioritization, not for customer-facing decisions.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_aRIcPwUMkb0xUYVmvska3Q