Sentiment Analyzer & Brand Monitor
Instantly analyze customer reviews, tweets, and comments with our free AI Sentiment Analyzer. 100% offline, privacy-first brand monitoring tool featuring live dashboard, pie chart, word clouds, filters, key insights, and CSV/export. Perfect for e-commerce marketers in the US, UK, CA, and AU.
Table of Contents
Wondering whether a batch of reviews, survey replies or messages reads as positive or negative overall? Paste the text and get a sentiment read with a polarity score for each line and a summary of the tone, so you can gauge the mood at a glance without reading every entry. It is useful for skimming a pile of customer feedback, checking the temperature of a comment thread, or sorting responses into rough buckets before a closer look. The analysis runs on a lexicon-based method entirely in your browser, so it is fast, needs no sign-up, and the text you paste is never sent to a server, which matters when the messages are private. Treat the score as a quick indicator rather than a verdict, and it stays available even offline once the page has loaded.
📊 Sentiment & Brand Studio
Read the mood of any text, score a pile of reviews at once, and find out which feature people are actually complaining about.
Emotional tone
Words driving the score
Sentence by sentence
| Sentiment | Sentence |
|---|
What people praise
What people complain about
Themes people keep raising
Enable theme grouping in the sidebar, then press the button to cluster your rows by meaning.
Every row
| # | Sentiment | Score | Text |
|---|
| Aspect | Mentions | Verdict | Example mention |
|---|
Aspects are picked out by grammar patterns, not by a trained model, so they are suggestions rather than certainties. Skim the example column before acting on any of them.
| Measure | Text A | Text B | Difference |
|---|
The lexicon engine scores as fast as you can type and needs nothing downloaded.
Groups reviews that mean the same thing even when they share no words — "dies before lunch" and "drains way too fast" land together. Needs a 23 MB model.
How the score is worked out
Each word carries a weight, and the sentence around it changes that weight. Not good is not a positive with a minus sign in front of it, and very good is stronger than good — both are handled explicitly.
Capital letters and exclamation marks push a score further from neutral, because people reach for them when they mean it. The word but matters too: in the food was fine but the service was appalling, the half after but carries most of the weight, which is how a reader would take it as well.
Related tools
- AI Text Summarizer — condense a long review set
- Word Counter & Case Converter — counts, case and keyword density
- Voice Typing Studio — dictate feedback, and translate on-device
- Browser AI Models Directory — what else runs in a browser
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🟢 Why the whole thing stays on your machine
A shop owner messaged me last year asking how to check the mood of about 400 Facebook reviews without pasting real customer names into some website he’d never heard of. That question is the reason this tool works the way it does. The model file downloads to your browser one time, and after that every line you type or upload is scored right there on your device. Close the tab and there is nothing sitting on a server somewhere — the reviews were never uploaded in the first place.
With DistilBERT (a two-way positive/negative model) the first line lands as negative — the “two days late” part outweighs the praise for a binary model. Switch to Twitter RoBERTa and that same line often shifts to neutral instead, because it has a middle class for mixed feelings. Same three reviews, two different readings — which is exactly why both models are here.
🔴 DistilBERT or Twitter RoBERTa — how I choose
Best for clean, full-sentence feedback — survey answers, email replies, product reviews written in proper sentences. It is smaller, downloads faster, and stays quick even on an older phone. The trade-off is that it only chooses positive or negative, with no neutral option.
Built for short, messy text — tweets, comments and brand mentions full of slang, emojis and @handles. It reads that kind of writing far better and adds a neutral bucket, so mixed lines are not forced into one extreme. It is a heavier download and a little slower on low-end devices.
🟡 Where it genuinely earns its keep
Drop a month of Google reviews in to see whether sentiment dipped after a price change or a new supplier.
Get a labelled sentiment column for a marketing assignment without touching Python or a paid API.
Sort a ticket export so the angriest messages float to the top and get answered first.
🔴 What it can’t do — the honest limits
- Sarcasm still beats it. A line like “great, another cracked screen” often scores positive because the model reads the word “great,” not your tone.
- It scores feeling, not truth. A fake glowing review still reads as positive — the model has no way to know the claim is false.
- Long paragraphs get cut off at roughly 512 tokens, so break up long rants into shorter lines for a fair reading.
- Translation can bend the result. The built-in translator is handy for foreign-language feedback, but idioms sometimes flip the sentiment, so treat translated rows as a guide.
- Keywords rank by frequency. After stop words are removed, a common product name usually tops the list — that is expected, not a bug.
Want the background on how these transformer models decide a piece of text is positive or negative? The Twitter RoBERTa model card on Hugging Face lays out exactly what it was trained on. And once you have your labelled CSV, our word and text counter helps you check review length before feeding a long batch in.
Last Updated: August 2026
🔴 What Each Tab Is For
Most sentiment tools give you one number and leave you there. A number is fine when you have three reviews. With three hundred it tells you almost nothing, because the useful question is never “are people happy” — it is “which part are they unhappy about, and how badly”. That is why this page has four tabs rather than one box.
Analyze — one passage, in detail
Paste a review or an email and you get a score from -100 to +100, a sentence-by-sentence breakdown, an emotional profile, a subjectivity reading, and the actual words that moved the number.
That last part matters more than it sounds. A score with no working shown is a black box you either trust or ignore. Seeing that hopeless -2.4 and gorgeous +3.0 did most of the work lets you judge whether the tool understood the passage or just tripped over one loud adjective.
Three behaviours are worth testing yourself, because they are where simpler tools fall over. Type it is not good and you get -34, not a positive. Type it is not bad and you get +43. And this sentence:
The food was excellent but the service was appalling
scores -23 rather than averaging out to zero, because what follows a contrast word is what a reader actually walks away with. Reverse the two halves and the verdict flips positive. That single rule fixes a lot of otherwise confusing results.
Brand Monitor — hundreds of rows at once
Paste one review per line and every row is scored separately, then rolled into a health figure out of 100. Sort worst-first and the twenty rows worth reading properly float to the top. Export the whole thing as CSV when you need it in a spreadsheet.
The praise and complaint chips underneath show which words appear most often in the positive rows and the negative rows. Treat them as a reading list, not a summary.
Aspect Analysis — the tab that earns its place
Overall scores average away the thing you needed to know. A product with a beautiful screen and a hopeless battery lands somewhere near neutral, which reads as “nothing to see here” while your customers are telling you exactly what to fix.
This tab splits each sentence into clauses, scores each clause separately, and attaches the verdict to whatever the clause was talking about. Feed it the sample review set and you get back a table where battery sits at negative, screen sits at positive, and delivery has its own separate verdict. That is a list you can hand to someone.
Compare — A against B
Two boxes, side-by-side metrics, and the difference on every row. Useful for checking whether a rewritten complaint reply actually landed softer, or whether this quarter’s feedback moved against last quarter’s. The built-in sample shows a blunt refusal against a warmer one — same decision, 55 points apart.
🟡 Two Engines, and When to Switch
The default engine is a lexicon. Every word carries a weight, and the words around it change that weight: negation flips it, intensifiers scale it, capital letters and exclamation marks push it further out. It is ready the instant the page loads, needs nothing downloaded, and is fast enough to score a thousand rows without you noticing.
The optional neural engine reads whole sentences rather than counting words, which makes it better at unusual phrasing and mild sarcasm. It is a one-time download of roughly 30 MB, stored by your browser, after which it runs with the network switched off. It runs in a Web Worker so the page stays responsive while it thinks.
Worth being precise about what that download is: your browser fetches a model file. It does not send your text anywhere. Analysis happens on your machine either way — the difference is only whether the maths is a weighted word list or a neural network.
🟢 Where This Will Let You Down
Sarcasm is still unsolved
“Great, another update that breaks everything” scores positive on the lexicon engine and often on the neural one too. Sarcasm depends on knowing what the speaker actually believes, which is not recoverable from the words alone. Any tool claiming to handle it reliably is overselling. Read your outliers.
Short text swings wildly
A three-word review gives one adjective total control of the score. The number is technically correct and practically useless. Below roughly fifteen words, treat the result as a hint.
Small samples produce confident nonsense
The Brand Monitor tab warns you below twenty rows, and it means it. A health score of 71 from twelve reviews is not a measurement, it is an accident. Fifty rows is a sensible floor before the number is worth repeating to anyone.
Aspect extraction is grammar, not understanding
Aspects are found by pattern — a candidate noun sitting next to an opinion, near words like the, my or is. It works well on short specific reviews and poorly on rambling ones. Occasionally it will surface a word that is not really a product feature at all. That is why every row carries an example: skim it before you act on it.
Domain and language limits
The lexicon is tuned for English consumer feedback. In a medical or legal context the same words carry different weight, and slang inverts things freely — “sick” and “wicked” are compliments in some registers and complaints in others. There is also no translation built in, deliberately: Voice Typing Studio handles that on-device, and translating before analysing is the cleaner order anyway.
🔴 The Reasoning Underneath
If you want to know why negation is genuinely hard, why word order breaks a bag-of-words model, and how neural sentiment differs from a weighted list, the companion piece covers it: how sentiment analysis actually works. The Wikipedia entry on sentiment analysis is a solid overview of the wider field.
❓ Frequently Asked Questions
Is my text uploaded anywhere?
No. Scoring runs in your browser. Open DevTools, watch the Network tab and press Analyse — there is no request carrying what you pasted, because none is made.
What does the score actually mean?
It runs from -100 to +100. Anything between -15 and +15 counts as neutral. The size matters as much as the sign: +20 is a mild lean, +80 is emphatic.
Does it understand “not good”?
Yes. Negation is checked within three words and flips the value while dampening it, so “not good” reads negative and “not bad” reads mildly positive.
Do I have to download the AI model?
No. The lexicon engine is the default and needs nothing. The neural model is an optional upgrade for nuance, and the tool works fully without it.
How many reviews before the health score means anything?
Fifty is a reasonable floor. Below twenty the tool says so on screen, because a figure from a handful of rows is noise dressed up as a measurement.
Can it handle sarcasm?
Not reliably, and neither can anything else. Sarcasm depends on knowing what the writer really thinks, which the words alone do not carry. Read your outliers.
What counts as an aspect?
A noun that an opinion is attached to within the same clause — battery, screen, delivery, staff. Each row shows an example mention so you can sanity-check it.
Does it work in other languages?
The lexicon is tuned for English. Other languages will produce a score, but not a trustworthy one. Translate first, then analyse, if the text is not English.
Can I get the results out?
Yes. The Brand Monitor tab exports every scored row as CSV with the row number, verdict, score and original text, ready for a spreadsheet.
