AI Customer Feedback Analyzer
Paste feedback and get sentiment, categories, and a short list of prioritized actions
AI Customer Feedback Analyzer is a Streamlit app backed by OpenAI models using structured outputs validated with Pydantic. For each input it aims to report sentiment, assign categories (such as product quality, service, pricing, delivery, UX, bugs, and suggestions), and return up to five actionable items with priority and reasoning tied to the text, so teams can skim decisions instead of rereading long threads.
When it is useful
You are triaging survey snippets, support quotes, or review blurbs; you want consistent JSON-shaped results for downstream tools; or you are demoing structured-output patterns to stakeholders. You supply an API key and run via uv (or pip) as documented.
What you can do
- Analyze pasted feedback blocks through the UI shown in the project screenshots.
- Rely on typed schemas so responses follow the enums and models defined in the repository.
- Extend prompts or categories in code when your taxonomy differs from the defaults.
Limits
- Models misread tone, sarcasm, or context; treat outputs as draft triage, not ground truth for HR, legal, or regulatory escalation.
- Sampling bias matters; if inputs are unrepresentative, “insights” will be too.
- Privacy: avoid uploading personal data you are not allowed to send to third-party APIs; retention follows your policies and vendor terms.
