AI Sentiment Analysis
Detect positive, negative, or neutral sentiment in any text. Powered by DistilBERT — runs locally in your browser, completely private.
First analysis takes ~15 seconds to download the AI model (~60MB). All subsequent analyses are instant.
Try an example
How to use
- 1
Type or paste any text — a review, tweet, feedback, or comment. Or click an example to try instantly.
- 2
Click Analyze Sentiment — the AI loads once (~15 seconds), then all future analyses are instant.
- 3
Read the sentiment (Positive, Negative, or Neutral) with a confidence score. Use Batch mode to analyze multiple texts at once.
Free AI Sentiment Analysis Tool — Detect Positive, Negative & Neutral Text
Analyze the sentiment of any text using AI. Detect positive, negative, or neutral tone with confidence score. Batch mode for multiple texts. 100% private, runs in your browser.
Skycally's AI Sentiment Analysis tool detects the emotional tone of any text — positive, negative, or neutral — using a DistilBERT model fine-tuned on the Stanford Sentiment Treebank. Paste any text from a product review to a social media post and get an instant sentiment label with a confidence percentage.
The AI model runs entirely in your browser using Transformers.js and WebAssembly — your text is never sent to any server. The first analysis downloads the model (~60MB) once; all subsequent analyses in the same session are instant, making it practical for analyzing multiple texts in succession.
Batch mode lets you analyze up to 50 texts simultaneously — paste one per line, click Analyze All, and see sentiment results stream in with a live summary showing the overall positive/negative/neutral breakdown. This is ideal for analyzing customer reviews, survey responses, or comment threads in bulk.
Common use cases include brand monitoring, customer feedback analysis, market research, academic sentiment studies, content evaluation, and any situation where understanding the emotional tone of written text provides value. The confidence score shows how strongly the AI classifies each text, helping you identify borderline cases that might warrant manual review.
Frequently Asked Questions
How accurate is the sentiment analysis?
The DistilBERT model achieves ~91% accuracy on the SST-2 benchmark for English text. Accuracy varies for informal language, sarcasm, and non-English text.
What languages are supported?
The model is fine-tuned for English text. Results for other languages may be less accurate as the model was primarily trained on English data.
How is the confidence score calculated?
The model outputs a probability score for each class. The displayed percentage shows how confident the AI is — above 90% is a strong signal, 70-90% is moderate.
Can I analyze multiple texts at once?
Yes. Use Batch mode and enter one text per line. Each is analyzed independently and the summary shows the overall positive/negative/neutral breakdown.
Is my text uploaded to a server?
No. The AI model runs locally in your browser using WebAssembly. Your text never leaves your device.
What is DistilBERT?
DistilBERT is a lightweight version of BERT (Google's language model) fine-tuned for sentiment classification. It's 40% smaller than BERT while retaining 97% of its performance.
Can it detect sarcasm?
Sarcasm is very difficult for any AI model. The tool focuses on surface-level sentiment signals — sarcastic text may be classified as positive when the true intent is negative.
Is this useful for businesses?
Yes. Common uses include analyzing customer reviews, monitoring brand mentions, evaluating survey responses, and understanding audience reactions to content or campaigns.
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