Product

Sentiment Classification Prompt for Reviews

Positive, Negative, Neutral, Mixed — overall-sentiment classification where the definitions resolve the "loved it BUT" reviews.

Open in Data Classification Prompt

Overview

Sentiment looks easy until the real reviews arrive: "great product, terrible support" breaks naive positive/negative prompts. This setup classifies overall review sentiment with four labels whose definitions carry the resolution rules — Positive means satisfied overall even with minor gripes, Mixed means strong positives AND strong negatives with no overall winner — under Best Match ambiguity, because in sentiment something must always be chosen; an "Other" pile defeats the purpose. High/medium/low confidence flags the genuinely torn reviews for human reading.

How to use this resource

Classify overall, not per sentence

The rules say judge the whole text — sentence-level mood swings are exactly what the Mixed definition absorbs.
Mind the Positive/Mixed border

"Satisfied overall, even if minor gripes appear" — the word minor is the border. Calibrate it to your data.
Read the low-confidence pile

Best Match never refuses, so low confidence is your only signal that a review genuinely resisted labeling.

Why This Works

A Mixed label with a real definition stops the classifier from rounding torn reviews up or down
Overall-judgment rules prevent last-sentence bias
Best Match suits sentiment: a forced choice plus a confidence flag beats an opt-out pile

Best for

Review streams where "loved it BUT" is the median review
Teams burned by binary positive/negative classifiers
Dashboards that need a Mixed category to stay honest

Not for

Extracting the pros and cons themselves — that's the Extraction Prompt Generator
Aspect-level sentiment (shipping vs product vs support) — that's a multi-label taxonomy, not overall sentiment

Use cases

Labeling product reviews for dashboards and trend lines
Separating genuinely Mixed reviews from Positive-with-gripes
Flagging low-confidence sentiment calls for human reading

FAQ

How does this prompt decide between Positive and Mixed for a "great product but terrible support" review?

The label definitions carry the rule, not the label names. Positive means "satisfied overall, even if minor gripes appear"; Mixed means "strong positives AND strong negatives with no overall winner." A great-product/terrible-support review lands Mixed only if neither side wins overall. Workflow step 2 flags the word minor as the border you calibrate to your own review data.

What format does the sentiment classification output come back in and where does it go next?

Each review returns a single JSON object with a "label" key and a "confidence" key, for example {"label": "Positive", "confidence": "high"} — no text around it and no markdown code fences, per the OUTPUT RULES. The connected data-classification-prompt tool generates this prompt; you run it in your own assistant, then feed the parsed label into dashboards or trend lines yourself.

Can I trust the high/medium/low confidence to automatically catch reviews that need a human?

Treat confidence as a triage flag, not a guarantee. The prompt states confidence is self-reported, not computed, where low means "the best available choice among the labels" under the Best Match policy that never refuses to classify. Workflow step 3 says the low pile is your only signal a review resisted labeling — a human still reads it and owns consequential calls. NewPrompt does not verify the labels.

Customize This Resource

Opens this setup in Data Classification Prompt. Generate to get the full classification prompt — then adjust the labels, ambiguity mode, and confidence output.

Open in Data Classification Prompt

Prompt Template

Copy it as-is, or use Open in Data Classification Prompt to load it pre-filled and customize it with your own context.

TASK
Classify the overall sentiment of product reviews.
Assign exactly one of the labels defined below to the text in the input.

LABELS
- Positive: The reviewer is satisfied overall, even if minor gripes appear.
- Negative: The reviewer is dissatisfied overall, even if something is praised.
- Neutral: Factual or indifferent — no clear satisfaction signal either way.
- Mixed: Strong positives AND strong negatives with no overall winner.

CLASSIFICATION RULES
- Read the entire text before deciding — the deciding signal may come late.
- Judge by content and intent, not by tone, length, or formatting.
- Match the text against the definitions, not just the label names.
- Use only the labels defined above — never invent a new label.

EDGE CASE RULES
- If the text fits more than one label, choose the one matching its primary purpose — what the writer mainly wants.
- If two labels fit equally, prefer the more specific definition over the more general one.

AMBIGUITY POLICY
- Always return the closest label, even when the fit is imperfect.
- Never refuse to classify.

CONFIDENCE
- With each label, report a confidence level: high, medium, or low.
- high = the text unambiguously fits the definition; medium = a good fit with minor uncertainty; low = the best available choice among the labels.
- Confidence reflects your certainty about the fit — it is self-reported, not computed.

OUTPUT FORMAT
Return your classification as a single valid JSON object with a "label" key and a "confidence" key.

EXAMPLE OF A VALID RESPONSE
{
  "label": "Positive",
  "confidence": "high"
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Return the label text exactly as defined above — no paraphrasing, no abbreviations, no new labels.

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Workflows that use this resource

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AI Customer Feedback Analysis Workflow

Turn a pile of reviews, surveys, or support comments into themes and priorities — extract the real signal, classify it by theme and sentiment, then summarize what's worth acting on.

3 steps 25–45 minutes

Tip: Save time by exploring related resources and tools that integrate with this resource.