TypeSafe Jev · signal out of the feedback pile

AI sentiment analysis — tag feedback by mood, theme, and whether it is actionable

Paste a review, an NPS comment, or any feedback and Jev returns the sentiment, whether it carries an actionable signal, and the primary theme — a typed tag with calibrated confidence, ready to roll up.

Tag some feedbackSee a real tag

Run sentiment / classification tagging now

This tool is pre-configured for sentiment / classification tagging. Paste your text and Jev returns the typed decision below — the same call the showcase example was captured from.

0 chars
Your typed decision appears hereCategory · yes/no · score, each with confidence
Typed decision + calibrated confidence — nothing to parse, nothing to hallucinate.

A real sentiment / classification tagging decision

Captured live from this exact tool — the text that was pasted, the typed decision Jev returned, and the measured latency and cost. Nothing here is mocked up.

Sentiment tagging212 ms

The product mostly works but the export keeps timing out on large files, and support took 4 days to reply. Pricing feels fair though.

SentimentNeutral88%
Neutral92%
Negative8%
Positive0%
Is ActionableYes · 95%
ThemePerformance99%
Performance100%
Pricing0%
Ux0%
Feature0%
typesafe/jev-1.13-20260917$0.000021

How sentiment / classification tagging works

01

Paste the feedback

A review, a survey open-text, an NPS comment, a churn note. Jev reads the raw sentence — mixed and hedged language included.

02

Jev tags mood, action, and theme

One pass returns the sentiment (positive / neutral / negative), whether the note is actionable, and the primary theme (pricing / ux / performance / support / feature).

03

Roll it up into a dashboard

Aggregate the theme tags to see what is actually driving negatives, and use the actionable flag to pull the comments a PM should read — the confidence tells you which rollups to trust.

Sentiment / classification tagging — frequently asked

How does this handle mixed feedback that praises and complains?

A star rating collapses "great product, terrible support" into one number. Jev tags the dominant sentiment and separately flags whether there is an actionable signal and which theme it belongs to, so a hedged comment does not get lost as a neutral 3-star.

What does the "actionable" flag actually surface?

It separates venting from a concrete issue or request. A comment that just says "meh" scores low; "export times out on large files" scores high — so a PM can read the 15 % of feedback that names a fixable problem instead of all of it.

Can I use my own theme taxonomy?

Yes. Pricing, UX, performance, support, and feature are typed criteria you can rename or extend to match how your team already slices feedback. No retraining is needed to change the theme set.

Is the sentiment label reliable enough to report on?

Each tag carries a calibrated confidence, so you can exclude low-confidence rows from a weekly rollup and keep the headline number honest. It is a probability you can filter on, not a single opaque label.

How fast can it tag a backlog?

The sentiment sample on this page returned in 212 ms. At that rate a backlog of thousands of comments tags in the time it takes to export the CSV.

Turn a feedback dump into a themed rollup

Paste one real review or NPS comment and see the sentiment, the actionable flag, and the theme Jev assigns — with confidence you can report on.

Tag some feedback

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Or start from the full use-case index or the generic Jev decision tool.