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Sentiment Analyzer

Runs in browser

Analyze text sentiment instantly — see positive, negative, and neutral scores with a per-word breakdown. No AI model required.

Last updated 01 Apr 2026

Analyze the emotional tone of any text with AFINN-based word scoring that highlights which words drive the sentiment. See overall sentiment score, normalised comparative score, and a colour-coded breakdown of positive and negative terms. Runs entirely in your browser — no data sent to any server.

Paste text above to analyse its sentiment
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How to use

  1. 1

    Paste or type your text

    Enter any text you want to analyse — a customer review, social media post, email draft, or any other content.

  2. 2

    View the overall sentiment

    The overall sentiment score (raw sum) and comparative score (normalised per word) appear immediately below the input.

  3. 3

    See which words drive the sentiment

    The per-word breakdown highlights positive words in green and negative words in red, showing the AFINN score for each matched word.

  4. 4

    Compare multiple texts

    Use the comparative score (not the raw score) when comparing texts of different lengths — it is normalised to account for word count.

Frequently asked questions

How does sentiment analysis work?
This tool uses the AFINN word list, which assigns a score from -5 (very negative) to +5 (very positive) to over 3,000 common English words. The overall score is the sum of all matched words. The comparative score divides by the total word count for a normalised result comparable across texts of different lengths.
What is a good sentiment score?
Scores depend heavily on text length and style. A positive raw score indicates net positive sentiment; negative means net negative. The comparative score is more reliable for comparison — anything above +0.1 is broadly positive, below -0.1 is broadly negative.
Is this accurate for all types of text?
AFINN works best for informal English text like reviews, social media posts, and customer feedback. It may be less accurate for technical, formal, or non-English text. It does not understand context, sarcasm, or idioms — for that, use the AI Sentiment Analyzer.
What does a score of 0 mean?
A score of 0 means the text is neutral — either no scored words were found, or the positive and negative words balanced each other out exactly.
What is the difference between score and comparative?
The score is the raw total and grows with longer text. The comparative score divides by word count, making it comparable between texts of different lengths. Use comparative when evaluating multiple pieces of content.
Is my text sent to a server?
No. All analysis runs locally in your browser. Your text is never uploaded, stored, or transmitted anywhere.
How is this different from the AI Sentiment Analyzer?
This tool uses the AFINN word list — instant, no model download, but limited to known scored words and blind to context. The AI Sentiment Analyzer uses DistilBERT, a neural network that understands negation, sarcasm, and sentence context, at the cost of a 67 MB model download.
Does it work on mobile?
Yes. The tool is fully responsive and works on any modern smartphone browser with no app required.
Can I use this to analyze non-English text?
The AFINN word list covers English only. For non-English text, most words will not be matched and the score will be near zero regardless of actual sentiment.

Paste any text and instantly see its emotional tone. This tool uses the AFINN

word list — a curated dataset of 3,382 English words each scored from -5 (very

negative) to +5 (very positive). The overall score is the sum of all matched

words; the comparative score divides by word count, making results comparable

across texts of different lengths.

The per-word breakdown colour-codes every matched word — green for positive, red

for negative — so you can see at a glance exactly which phrases are driving the

tone. Unscored words (neutral or unknown) are shown without highlighting.

Common use cases include: analyzing customer reviews to track product perception,

scoring social media comments for brand sentiment, checking email drafts for

unintended negative tone, evaluating survey responses at scale, and teaching

students about computational linguistics.

For deeper accuracy on nuanced language — sarcasm, negation ("not bad"), or

context-dependent phrasing — try the AI Sentiment Analyzer which uses a

DistilBERT neural network trained on millions of text samples.

All analysis runs entirely in your browser with no data sent to any server.

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