Paste customer reviews and analyze sentiment distribution. Categorize positive, negative, and neutral themes with word cloud visualization.
Customer reviews pile up faster than anyone can read closely, and one angry three-star review tends to outweigh ten quiet five-stars in memory. Paste your review text into this tool and it sorts each review into a positive, negative, or neutral bucket, shows the distribution as percentages, and pulls out the recurring themes in each bucket with a word cloud of the most frequent terms. The thing most users get wrong is reading the split as a verdict on the product: it measures what the text says, and reviews without a clear positive or negative signal land in the neutral bucket, which most users never read.
Each review is classified once and placed in one of the three buckets, so the distribution always describes the exact sample you pasted, not the product, the store, or the brand overall. Classifications reflect the text as written: edited, trimmed, or partially pasted reviews produce a distribution for the edited version, not the original.
The percentages answer 'how many', not 'why'. The theme view answers 'why' by grouping the terms that recur within one bucket's reviews, so you can see what praise and complaints are actually about. In the word cloud, larger words are the ones that appeared most often in the pasted text; frequency drives size, not importance.
Sentiment classification of non-English text is generally far less reliable. Test before trusting it: paste a handful of reviews whose sentiment you already know; if the buckets match your own reading, the language is handled well enough, and if they do not, treat the full run as unreliable.
There is no honest single accuracy figure, because it depends entirely on the language you paste. Run your own check: paste ten reviews you would label by hand and count how many match. Sarcasm, mixed feelings, and short factual updates are where automated sentiment most often disagrees with a human reader, so expect the disagreements there, not in obvious praise or complaints.
The behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
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