Labels text feedback as positive, neutral or negative, learns domain language from labelled examples and supports deeper LLM-assisted analysis.
The examples below show where this project can be used, what decision it supports, and how a user can test the scenario in the sandbox.
Where it fits: Classify comments from tickets, app reviews, surveys or call-centre notes into sentiment categories.
Decision supported: Find negative drivers quickly and prioritise service recovery.
How to test it: Load sample data, train the model and ask for negative drivers.
Where it fits: Analyse patient comments about waiting time, service quality, billing or communication.
Decision supported: Support quality improvement while tracking patient experience themes.
How to test it: Upload feedback data and map comment and label columns.
Where it fits: Score beneficiary feedback, hotline comments or partner complaints for sentiment and urgency themes.
Decision supported: Improve accountability to affected populations and donor reporting.
How to test it: Use Groq-assisted questions for complex theme summaries.
Where it fits: Classify product reviews and extract recurring positives and negatives.
Decision supported: Prioritise roadmap fixes based on user sentiment.
How to test it: Export labelled data for dashboarding.
Where it fits: Analyse open-text employee feedback at aggregate level without exposing unnecessary personal details.
Decision supported: Identify morale and engagement themes for HR action.
How to test it: Use aggregated comment data and review sentiment distribution.
Where it fits: Understand sentiment around services, campaigns or policy communications.
Decision supported: Guide communication improvements and issue response.
How to test it: Ask the assistant for executive-ready findings.
The examples above were revised against current industry and research references on how this class of analytics solution is used in practice.