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.
Feedback in bulk is noise until someone structures it. Reading a thousand reviews by hand doesn't scale, and pasting them into a model with 'what do people think?' gets you a vague vibe and a few cherry-picked quotes. Useful feedback analysis is mechanical in the right way: pull the concrete signal out of each comment, classify it consistently by theme and sentiment so patterns surface, and summarize the result into something a product or support lead can act on — counts and priorities, not a mood.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Extract the signal from the noise
Pull the concrete points out of each piece of feedback — the specific complaint, request, or praise — so you're analyzing claims, not raw prose.
Classify by theme and sentiment
Label each point consistently so patterns become countable: which themes recur, which carry negative sentiment, where the volume actually is.
Summarize what to act on
Roll the labeled feedback into a summary that leads with priorities — themes by volume and severity — not a list of nice quotes.
A bulk of raw feedback becomes ranked themes with sentiment and volume behind them — a summary a product or support lead can prioritize from, instead of a hunch supported by a few quotes.
Choose feedback analysis when you have many comments and need themes and priorities across all of them; choose the Support Workflow when you're handling one ticket — triage, reply, log. This workflow aggregates signal; support runs per-ticket. Volume and a ranked picture point here.
No. Step 1 pulls the actual point of each comment and step 2 classifies meaning, not words — so 'checkout is broken' and 'can't complete my order' land in the same theme.
The output is a prioritized theme summary — recurring themes ranked by volume and severity, with sentiment attached — not a transcript of quotes. A product or support lead reads it to decide what to fix first. Steps 1 and 2 feed it; step 3 leads with the priorities that matter.
Run the three prompts in order in your own AI tool: extract concrete points from each comment, classify each by theme and sentiment, then summarize into ranked priorities. NewPrompt supplies the prompts and the step order; you paste in your feedback and run each stage yourself.
Spot-check the labels before anyone prioritizes: pull a sample of classified points and confirm each theme and sentiment matches what the comment actually says, and that near-duplicates like 'checkout is broken' and 'can't complete my order' share a theme. Classification surfaces patterns; you verify them.
Bring the raw feedback itself: reviews, NPS or survey responses, or support comments, as a plain list — one comment per row. No fixed schema is needed since step 1 extracts the points. More volume sharpens the theme counts; a handful of comments won't surface reliable patterns.
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