🧠 AI-Powered · Runs in Browser

AI Sentiment Analyzer

Detect the emotional tone of any text — positive, negative, or neutral — using a real AI model that runs entirely in your browser. No API key, no signup, no data sent anywhere.

Sentiment Analysis
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DistilBERT Sentiment Model
~67MB · Downloads once, then cached in your browser · Runs 100% locally · No data leaves your device
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What Is Sentiment Analysis?

Sentiment analysis is a branch of Natural Language Processing (NLP) that identifies the emotional tone behind text. It classifies text as positive, negative, or neutral — and can detect confidence levels for each classification.

This tool uses DistilBERT, a distilled version of Google’s BERT model trained on millions of text samples. It runs entirely in your browser using WebAssembly and ONNX runtime — no server required.

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100% Private

Your text never leaves your browser. The AI model runs locally on your device.

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No API Cost

No OpenAI, no Google AI, no subscription. Completely free, powered by open-source AI.

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Sentence-level

Breaks your text into sentences and analyzes each one individually for granular insight.

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After First Load

Model is cached in your browser. Second time is instant — no download needed again.

Frequently Asked Questions

The AI model file is about 67MB. It downloads from Hugging Face servers once, then gets cached in your browser’s storage. Every subsequent visit uses the cached version and loads in under a second.
For sentiment classification specifically, DistilBERT achieves ~91% accuracy on standard benchmarks — comparable to much larger models. It is specialized for this task, so it performs extremely well for sentiment analysis even though it cannot do general conversation.
This model is trained primarily on English text and works best with English. For other languages, results may be less accurate. A multilingual sentiment model would require a larger model file.
Common uses include: analyzing customer reviews before publishing, checking the tone of emails before sending, monitoring social media comments, evaluating user feedback, and understanding whether marketing copy sounds positive or off-putting.

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