Sentiment analysis, right in your browser
Paste reviews, survey answers, or feedback — one per line — and get instant positive/negative scoring. Honestly labeled: lexicon-based and fully client-side, which is why nothing needs uploading.
Runs 100% in your browser — this page makes no network request with your data.
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Paste text with one response per line (or load the sample reviews).
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Each line gets a sentiment score; totals summarize positive / neutral / negative.
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Export the scored lines to the workspace for AI-grade analysis with segments and charts.
Worked example: triaging 8,000 post-purchase survey comments
A customer-experience analyst has 8,000 open-ended answers from a post-purchase survey and needs a fast read before the weekly meeting. They paste the comments into the sentiment analyzer, one response per line.
Each line is scored against the positive and negative word lexicons, and the totals summarize how many responses landed positive, neutral, and negative. Within seconds the analyst can see that returns-related comments skew sharply negative while delivery comments skew positive, enough to know where to dig. Because the scoring is lexicon-based and runs entirely in the browser, none of the customer feedback is sent to an outside API, which keeps a privacy-sensitive dataset in-house.
Lexicon scoring is deliberately a first pass, weaker on sarcasm and mixed sentences, so the analyst exports the scored lines to the workspace. There the responses are analyzed with full context, crossed against structured survey fields, and turned into a shareable report with charts and segments.
Sentiment analysis scores text as positive, negative, or neutral so you can read the mood of a pile of feedback without reading every line. The tool above does it the classic way: it matches each line against curated positive and negative word lists and tallies the result, entirely in your browser, so the reviews or survey answers you paste never leave your device.
That lexicon approach is fast, transparent, and private, and it has real blind spots. Knowing exactly what it catches and what it misses is the difference between using it well as a triage step and trusting it for something it was never built to do.
What lexicon-based sentiment analysis can and cannot do
A lexicon scorer works by looking up each word in positive and negative dictionaries and summing the hits. That makes it instant and fully explainable, you can see exactly why a line scored the way it did, but it also means it reads words, not meaning. The sentiment of a phrase is not the sum of its words, and that is precisely where it fails.
Here is the honest split of what the method handles and what it does not:
| Handles well | Struggles or fails |
|---|---|
| Clearly worded praise or complaint | Sarcasm: 'Oh great, another outage' scores positive on the word great |
| Large batches, scored instantly | Negation: 'not good' scores positive because good is in the lexicon |
| Direction and rough proportions | Domain language: 'love waiting 40 seconds for a dashboard' reads as positive |
| Full privacy, nothing uploaded | Mixed sentences: 'fast shipping but broken item' averages to noise |
| Transparent, auditable scoring | Comparatives and context: 'better than the last one' has no anchor |
When lexicon scoring is good enough, and when it is not
It is good enough whenever you need direction fast and can tolerate individual errors because you are looking at aggregates. Triaging a batch of survey open-ends to see whether returns comments skew negative, getting a first read on a week of reviews, or sorting hundreds of responses into rough buckets before a meeting are all exactly the job. The individual misfires wash out across a few hundred lines; the overall lean is usually right.
It is not good enough when a single score drives a decision or reaches a person. Do not route an individual support ticket on its lexicon score, publish a per-customer sentiment number, or report a precise percentage as if it were measured. For those, treat the score as a hint to verify, not a verdict. The safe framing is triage, not measurement: it tells you where to look, and you confirm what you find by reading the actual lines.
How to read the scores
Read the aggregate split first, the counts of positive, neutral, and negative, because that is where the signal is most reliable. A batch that lands 60 percent negative is telling you something real even if a handful of those lines are misclassified.
Be skeptical of two things. First, the magnitude of any single line: a strongly positive score often just means the sentence stacked several lexicon words, not that the feeling is intense. Second, the neutral bucket, which is a catch-all. A line lands neutral when it has no lexicon words, when positive and negative cancel out, or when it is off-topic or too short to score. Before you conclude that a third of your feedback is indifferent, open the neutral lines, they are often mixed sentiment or substantive comments the lexicon simply could not read.
From sentiment to themes: the triage-to-report workflow
Sentiment is step one, not the analysis. A positive-negative split tells you the mood but not the reason, and the reason is what you can act on. The practical path from a pile of comments to something you can present runs in four moves.
The workflow that turns feedback into a finding
- Triage by polarity: score the lines here and split them into positive, negative, and neutral so you know which pile to read first.
- Read the negatives and code themes: group the complaints into recurring reasons, shipping, price, a specific bug. This qualitative step is the heart of it; see qualitative data analysis for coding open-ends properly.
- Quantify the themes: count how often each theme appears so you can rank them, the word frequency counter gives you the dominant terms and phrases to anchor the coding.
- Report the result: cross the themes against structured fields (segment, plan, region) and package the finding into a shareable report. That final synthesis is what survey analysis and the workspace are built for.
When to reach for something stronger
If your text is full of sarcasm, heavy negation, or domain-specific phrasing where the plain meaning inverts the sentiment, a lexicon will mislead you no matter how you tune it. That is when model-based approaches earn their cost. A transformer or large-language-model classifier reads context and handles negation and irony far better, and aspect-based sentiment can separate feelings about different parts of one sentence, praising shipping while panning the product.
The honest tradeoff is that those methods send text to a model, cost more, and are harder to audit line by line. For high-stakes or nuance-heavy work they are worth it. For fast, private triage of clearly worded feedback, lexicon scoring in the browser remains the right first tool, and it feeds cleanly into the deeper analysis when you escalate.
Frequently Asked Questions
Everything you need to know about using AnalyzeData.
It scores each line against curated positive and negative word lexicons — a transparent, classical method that runs instantly in your browser. It is deliberately not a black-box model, which also means no data ever leaves your device.
Good for quick triage of clearly-worded feedback; weaker on sarcasm and mixed sentences. For serious survey work, export the scored lines to the workspace, where analysis runs with full context — or see our survey analysis solution.
Yes, as a first pass: paste the responses, review the split, then send the scored set to the workspace to cross it with your structured answers.
Yes, that is the point of this tool. Scoring uses curated positive and negative word lexicons and runs entirely in your browser, so the reviews or survey answers you paste are never transmitted to an external service or model. It is a transparent, classical method rather than a black-box API, which is exactly why nothing needs to leave your device.
Score the text here for a quick positive and negative split, then export the scored lines to the workspace. There the results become charts and segments, can be crossed with your structured survey fields, and are packaged into a themed, shareable report with a live link and PDF, the deliverable the quick in-browser pass is meant to feed.
Neutral is a catch-all, not a confident reading of indifference. A line scores neutral when it contains no words from either lexicon, when positive and negative words cancel out, or when it is off-topic or too short to score. Before treating neutral responses as apathy, open and read them, they frequently hold mixed sentiment or substantive feedback the lexicon simply could not interpret.
Poorly. A word-list scorer sees good in the positive lexicon and often scores 'not good' as positive, because it does not track that the not flips the polarity across the phrase. Negation, sarcasm, and comparatives all share this root cause: the feeling of a sentence is not the sum of its individual words. For text heavy in negation, verify the scores by reading, or use a context-aware model.
Enough that aggregates smooth out individual errors, typically a few hundred lines or more. Lexicon scoring makes mistakes on single comments, but across a large batch the overall positive-to-negative lean is usually reliable. On a handful of responses the noise dominates, so at small volumes you are better off just reading them than trusting a computed split.
No, treat it as a sortable label rather than a verdict. The score's real value is letting you rank and group hundreds of comments instantly; its accuracy on any one line, especially with sarcasm, negation, or domain phrasing, is exactly what you should confirm by reading. Use it to decide where to look, then let the actual text settle what is true.
When a table isn't enough
The workspace runs verified AI analysis on the same file and turns the results into a report you can send.
Analyze this data instead