Sentiment analysis tool
Get a quick read on customer feedback.
Paste English text. See the tone, score and words behind it.
Your result
LEXICON ANALYSISA quick read on the tone.
Paste English text and analyze it to see its score and the words behind it.
No text upload.
One review is a starting point.
Follow the wider conversation with ChatterBeam: collect public brand mentions, filter feedback and set up alerts.
Can I check sentiment without uploading my text?
ChatterBeam’s free sentiment analysis tool scores one pasted English review or comment in your browser, up to 5,000 characters. It uses a word-and-emoji dictionary, not a cloud AI model. The result includes a sentiment label, total score, score per token and the words that contributed.
No text upload is needed for analysis. The tool does not import CSV files, fetch Reddit comments or track mentions automatically. Those are different tasks from checking a single text sample.
Understand the score. Keep the context.
This free sentiment analysis tool matches English words and emoji against a scored dictionary. Positive matches add to the score; negative matches subtract. Basic negation can reverse a word’s contribution. A score of +6 across 12 tokens becomes +0.50 per token, not “50% positive.”
Use it as a first pass on a review, support comment or social post. It does not understand irony, infer intent or guarantee sentiment accuracy. For example, “Great, another outage” may look positive to a dictionary while expressing frustration.
Powered by the MIT-licensed sentiment library, using AFINN and emoji scores. This is not the same analysis pipeline as ChatterBeam’s monitoring workspace.
Sentiment analysis examples you can reproduce
These illustrative samples are calculated with the same scoring function as the tool. They are not an accuracy benchmark. Paste any sample above to check its result.
| Text | Result / score | How to read it |
|---|---|---|
| excellent | Positive 3 total · 3.00 per token | A clearly positive dictionary match. |
| not good | Negative -3 total · -1.50 per token | Basic negation reverses the contribution of good. |
| excellent but terrible | Mixed signals 0 total · 0.00 per token | Opposing signals remain visible even when the total cancels out. |
| The meeting is on Tuesday | No signal 0 total · 0.00 per token | No matched words is not proof of neutral sentiment. |
| Great, another outage | Mixed signals 1 total · 0.33 per token | Mixed signals with a positive total still cannot establish whether this is sarcasm. |
Which sentiment analysis method should I use?
Choose a local lexicon tool for a quick, explainable check of one short English comment. Inspect the matched words and read the original context. ChatterBeam’s free tool fits this task, but does not provide a confidence probability, aspect-level sentiment or batch reporting.
Consider a contextual model when you need to interpret nuanced language or separate opinions about pricing and support. Evaluate it on representative, human-reviewed examples; a model is not automatically accurate. Check where inference runs and what the provider retains before submitting customer information.
Use a monitoring workflow when the task is finding new conversations over time. Collection coverage, collection dates, duplicates and missing sources affect what a brand-level sentiment summary can mean. Start with our social listening methodology and source coverage guide.
From a text sample to customer insight
- Paste one English review or comment, up to 5,000 characters.
- Analyze it and inspect the contributing words, including negated terms.
- Read the original context before deciding what to follow up on.
For the next step, read our guides to customer feedback analysis, Reddit monitoring and competitor monitoring.
Frequently asked questions
Is this sentiment analysis tool free?
Yes. Analyze up to 5,000 characters at a time without signing up. Analysis runs in your browser and does not call a paid AI service.
Is my text uploaded or saved?
The analyzer does not upload or save your text. Text stays in page memory until you clear it or leave. Standard site analytics may record page views and button interactions, but not your text or analysis results.
How is the sentiment score calculated?
This English lexicon-based tool uses the open-source sentiment library with AFINN word scores and emoji scores. It adds matched token scores, with basic negation handling. The normalized score is the total divided by token count; it is not a probability or accuracy percentage.
What do mixed signals and no signal mean?
Mixed signals means positive and negative matched scores both appear, with the smaller absolute total at least half the larger. No signal means no scored tokens were found, not that the text is objectively neutral.
Can it understand sarcasm or other languages?
Not reliably. This is a first-pass English text check, not a contextual AI model. Sarcasm, domain-specific language, long-range negation and non-English text can produce misleading results. Review the original text before acting.
Can I analyze Reddit comments with this tool?
Yes, you can paste one English Reddit comment, up to 5,000 characters, for a local dictionary-based check. This free tool does not retrieve comments from a Reddit URL. A single comment score does not represent the sentiment of a community or brand.
Can I upload a CSV or score each review separately?
No. This tool accepts one text sample, not CSV files or row-by-row batches. Pasting multiple reviews produces one combined score and can hide opposing opinions. Analyze reviews individually when you need separate results.
Does a positive score mean a customer is satisfied?
Not necessarily. The score measures matched words, not customer satisfaction or a probability. Sarcasm and mixed opinions may be misclassified. Read the original feedback and validate important conclusions with human review.