AI Sentiment, Keywords, and Executive Summaries in RecRam
What this guide covers RecRam AI analyzes each response so you can review faster. This guide covers three of its outputs: sentiment analysis, keyword extraction, and executive summaries. It also shows how to use sentimen
Last reviewed: October 9, 2026
What this guide covers
RecRam AI analyzes each response so you can review faster. This guide covers three of its outputs: sentiment analysis, keyword extraction, and executive summaries. It also shows how to use sentiment to prioritize follow-ups and what to do when a result looks wrong.
AI results speed up qualitative review, but they don’t replace it. Use them with your own business context when you make final decisions.
Sentiment analysis
Sentiment analysis labels each response as showing positive, negative, or mixed signals. Use it to see quickly how a group of responses leans, and to find the ones that need attention.
Keyword extraction
Keyword extraction surfaces repeated words and recurring themes across responses. Use it to spot what many respondents mention, then read the responses behind each theme.
Executive summaries
Executive summaries cut manual review time for long responses. Read the summary first, then go to the full response when a decision depends on it.
Prioritize follow-ups using sentiment
- Decide what you want the AI output to help you decide.
- Review transcript, sentiment, keywords, and summary together for each response.
- Tag recurring themes and assign follow-up actions by urgency.
- Cross-check critical responses manually before you act.
Troubleshooting
For each of the problems below, reproduce the issue with one controlled test response, cross-check the result against the recording, and change one thing at a time.
A sentiment result looks inaccurate
Watch or listen to the response and compare it with the label. Sentiment is one signal; read it together with the transcript, keywords, and summary rather than on its own.
Keyword output is too generic
Keywords reflect the repeated words and themes in the responses. Review them across the full response set, not one response at a time, and read the transcripts behind any theme you plan to act on.
An executive summary is missing or too short
Summaries are most useful on long responses. Check the full response and its transcript, and reproduce the issue with one controlled test response.
Common mistakes
- Treating AI output as final truth without checking it against context.
- Reviewing only one AI signal instead of the full response set.
- Skipping periodic quality reviews with real examples.