text Faster on WebGPU

Sentiment Analysis

Classify text as positive or negative — runs entirely on your device.

First run downloads ~62 MB. The model is cached after the first use, then runs offline. Manage downloads on the settings page.
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About Sentiment Analysis

Sentiment Analysis classifies a piece of text as positive or negative, running an NLP model entirely on your device. Use it for a quick read on the tone of a review, a comment, or a support message without sending that text to any server. It returns a structured JSON verdict you can act on or chain into other steps.

Category
text
Input
Accepts: text/plain.
Output
Outputs: application/json.
Cost
Free, runs in your browser
Memory
medium
Install group
nlp-standard
Privacy: Sentiment Analysis runs entirely on your device. Files you provide never leave your browser — no uploads, no server, no tracking. The page works offline once loaded.

Common uses

  • Gauge whether a batch of product reviews skews positive or negative before reading them all
  • Triage support tickets by flagging the angry ones for faster response
  • Check the tone of a draft email or message before you hit send
  • Score survey free-text answers as a quick pulse on customer mood
  • Sort social comments into positive and negative buckets for a content report
  • Spot-check the emotional lean of testimonials you plan to feature

Frequently asked questions

What does the output look like?

JSON with a positive or negative classification, so you can pipe it into a script or another tool rather than eyeballing it.

Does my text get sent anywhere?

No. The classification model runs in your browser on your device, so the text you analyze never leaves your machine.

Does it detect neutral or mixed sentiment?

This free in-browser version classifies along a positive/negative axis. For neutral handling plus sarcasm and mixed tone, the hosted Sentiment Pro tool is more nuanced.

How long can the input be?

It accepts plain text and works best on a single passage at a time. Very long documents are better split into sentences first.

What languages does it handle?

It is tuned primarily for English. For other languages, translate first, then analyze.

Is it accurate on sarcasm?

Lightweight heuristic models miss sarcasm and irony. If that matters, use the hosted Pro version, which handles tricky tone better.

Keywords

  • sentiment
  • emotion
  • positive
  • negative
  • classify
  • nlp
  • opinion

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