Classify comment sentiment with traceable evidence

Label supplied comments consistently while preserving mixed opinions, unclear cases and the exact words supporting each label.

PLPromptByte Library@promptbytePublished Sep 8

TextData & Analysis #qualitative data #sentiment analysis #text classification

Version 1 of 1 · published Sep 8, 2026Version 1 · only version

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Prompt

Version 1 · 2 variables · 2 required

Fill in the values below — the customized prompt updates as you type. marks a required value.

Text records{{text_records}}

Comments with stable IDs and any necessary context. Default: Illustrative comments: C1: Reports load quickly now. C2: The charts are useful, but exporting still fails. C3: Well, that was something. C4: Where is the download button?

Labeling rules{{labeling_rules}}

Target subject, allowed labels and operational definitions. Default: Target: reporting product experience. Labels: positive, negative, mixed, neutral, unclear. Questions without an expressed evaluation are neutral unless context says otherwise.

Customized prompt

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Resolved user prompt10 lines

Classify sentiment in these comments.

Text records with IDs: Illustrative comments: C1: Reports load quickly now. C2: The charts are useful, but exporting still fails. C3: Well, that was something. C4: Where is the download button?
Subject and labeling rules: Target: reporting product experience. Labels: positive, negative, mixed, neutral, unclear. Questions without an expressed evaluation are neutral unless context says otherwise.

Assign each record one permitted label based only on its expressed opinion about the target subject. Give a short evidence excerpt and concise rationale. Preserve mixed sentiment and use an unclear label where context, sarcasm or an unstated target prevents a defensible assignment.

Do not infer a person's hidden emotional state, diagnosis, demographics or intent. Keep topic/aspect separate from sentiment, and flag comments that discuss more than one aspect.

Summarize label counts with the supplied-record denominator and list ambiguous records for human review. Do not treat this sample as population opinion, manufacture confidence probabilities or claim a validated classifier.

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Usage notes

Define labels before running and review ambiguous cases. Counts describe only the supplied records; the prompt is not an evaluated classifier.

Adapted by PromptByte from prompts/analysis/sentiment-analysis-expert.md by AJ Geddes, under MIT. Narrowed Sentiment Analysis Expert to evidence-backed record labels. Removed emotion-intensity inference, invented confidence levels, trend claims and unsupported competitive benchmarks. No model run or example result is claimed.

Original MIT notice:

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

code · sql
```
**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'
```
Code preview: **Assumptions** - `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed. - "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today. - Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied. - Cancelled orders are excluded. If `status` uses different values than `'
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Classify comment sentiment with traceable evidence

Example output

Prompt

Customized prompt

Resultsfor version 1

Something wrong with this prompt?

SQL query generator from a plain-English request

Audit SQL joins for duplicate rows and inflated totals

Design a forecast backtest that respects time and information

Critique an existing chart for misleading interpretation

Reshape a wide CSV into tidy records without losing meaning

Define retention cohorts before calculating a heatmap

Discussion 0

Example output

Prompt

Customized prompt

Resultsfor version 1

Something wrong with this prompt?

Related prompts

SQL query generator from a plain-English request

Audit SQL joins for duplicate rows and inflated totals

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Discussion 0