Sentiment Analysis
The computational classification or scoring of opinions, attitudes or emotional polarity expressed in text or other data.
It supports accountable use of AI by making capabilities, evidence and limitations visible to decision-makers.
A cross-functional team applies Sentiment Analysis in a production initiative, defines ownership and success criteria, tests representative scenarios, monitors outcomes and records corrective actions before scaling.
Model outputs remain probabilistic and context-dependent. Evaluation results can degrade over time and do not by themselves establish fairness, safety or legal compliance.
The computational classification or scoring of opinions, attitudes or emotional polarity expressed in text or other data. The technique operates on data and model outputs within a defined use context. Evaluation should cover accuracy, representative performance, human oversight, privacy, security, documentation and monitoring after deployment.
Data Engineering, Experience Design
NIST AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework; ISO Artificial Intelligence standards — https://www.iso.org/sectors/it-technologies/ai